Asynchronous Pulse Code Multiple Access (APCMA) has been proposed as a pulse-based LPWA technology for massive IoT, offering robustness against collisions. However, in ultra-dense environments, pulse overlaps cause misdetections. This paper proposes an enhanced CSS-APCMA decoding method that leverages per-message chirp frequency-offset consistency and accounts for clock jitter. The receiver was implemented using a USRP and GNU Radio, and large-scale experiments were conducted with 1,000 transmitters operating in the 920 MHz band. The results demonstrate that the proposed method maintains a high reception rate while significantly suppressing misdetections and achieving higher data rates compared to conventional CSS-APCMA in ultra-dense environments.

In millimeter-wave (mmWave) Vehicle-to-Everything (V2X) communications, dynamic mobility often leads to severe degradation in communication quality due to transitions between line-of-sight (LoS) and non-line-of-sight (NLoS) conditions or interference from surrounding vehicles. To address this challenge, we propose a handover (HO) control scheme across multiple base stations (BSs), and evaluate two distinct approaches: cooperative transmission and null-space expansion. In the cooperative transmission scheme, multiple BSs synchronously transmit identical data, transforming interference into a desired signal to enhance reception during HO. In contrast, null-space expansion suppresses interference by steering nulls toward undesired directions, which is particularly effective during vehicle approach. Through simulations with three BSs, two vehicles, and two blocking vehicles under three scenarios (interference only, blockage only, and combined), we show that cooperative transmission consistently achieves the highest SINR across cell boundaries and during blockage events, whereas null-space expansion provides partial gains under specific conditions. These results highlight the complementary strengths of both approaches and provide practical insights into the design of robust mmWave V2X HO.

The widespread deployment of 5G networks has drawn growing attention to the optimization of wireless communication systems. Among various techniques, quantum-based approaches are distinguished by their remarkably fast computation time compared to conventional optimization methods. In this paper, we propose a quantum-inspired approach to the wireless resource allocation problem. The problem is formulated as an Ising model and solved using both Coherent Ising Machine (CIM) and D-Wave’s quantum annealer. Furthermore, the performance of these quantum-based methods is compared with that of the Simulated Annealing (SA) algorithm.

In Low-PowerWide-Area Networks (LPWANs) such as LoRa, the preamble is essential for detecting highly attenuated signals. Its repetitive pattern allows a receiver to identify the presence of the signal and its precise starting point. However, in ultra-low Signal-to-Noise Ratio (SNR) environments, the preamble becomes undetectable as it is buried in strong noise, causing the entire detection process to fail. Although existing methods, such as those based on preamble symbol energy accumulation or deep learning-based spectrogram restoration, have been proposed, their performance remains limited under these extreme conditions. To address this limitation, this paper proposes a novel two-stage preamble detection scheme. The first stage employs a Convolutional-Transformer Encoder-Deconvolutional network that leverages self-attention to capture the distinct linear patterns of chirp signals even in the presence of severe noise. In the second stage, a classifier determines the presence of the preamble. Experimental results demonstrate that our proposed method significantly outperforms conventional approaches, lowering the minimum required SNR for preamble detection. To validate its performance, we utilized metrics including True Positive Rate (TPR) and F-scores. Under these evaluations, our scheme achieves a detection accuracy of over 90% in the ultra-low SNR range of -21.7 dB to -24.3 dB, confirming its robustness and practical viability.

The realization of sustainable logistics and regional transportation services requires the deployment of automated driving technologies, which in turn demands further latency reductions in mobile networks. However, one of the major challenges in existing mobile networks, including 5G, is addressing communication quality degradation, such as latency spikes, that occur during inter-cell handovers (HOs). We have been investigating a multi-band redundant vehicle-to-network-to-vehicle (V2N2V) communication scheme as a potential solution to mitigate the impact of such degradation during inter-cell HOs in mobile-network-based inter-vehicle communications. This paper presents a field trial of the proposed multi-band redundant V2N2V scheme under single mobile network operator (MNO), assuming its application to bidirectional real-time control message exchange in automated Bus Rapid Transit (BRT) platooning. Experimental results conducted over a commercial 5G network demonstrate that the proposed scheme can suppress latency spikes during inter-cell HOs. These findings indicate that multi-band redundant V2N2V is a promising approach for achieving stable low latency in V2N2V communication.

With the advancement of quantum computing, quantum image representations (QIRs) have been widely studied as platforms for quantum image processing. While previous studies have focused on theoretical qubit counts and circuit complexities, there has been limited quantitative evaluation of image reconstruction performance from quantum states. In this study, we clarify the theoretical upper bounds of restoration performance for three representative QIRs: qubit lattice (QL), flexible representation of quantum images (FRQI), and novel enhanced quantum representation (NEQR). Also, we validate them through large-scale GPU-based simulations using cuQuantum. Specifically, we (1) derive a prediction formula for PSNR in probabilistic schemes like QL and FRQI, independent of image content and size; (2) show that deterministic schemes like NEQR require a number of samples following a harmonic number for complete restoration; and (3) perform large-scale simulations to verify these findings. These results provide fundamental insights for designing QIRs and their applications on NISQ devices.

With the increase in demand for Internet of Things (IoT) applications, the number of IoT devices has drastically grown, making spectrum resources seriously insufficient. Transmission collisions and retransmissions increase power consumption. Therefore, even in long-range (LoRa) networks, selecting appropriate transmission parameters, such as channel and transmission power, is essential to improve energy efficiency. However, due to the limited computational ability and memory, traditional transmission parameter selection methods for LoRa networks are challenging to implement on LoRa devices. To solve this problem, a distributed reinforcement learning-based channel and transmission power selection method is proposed, which can be implemented on the LoRa devices to improve energy efficiency in this paper. Specifically, the channel and transmission power selection problem in LoRa networks is first mapped to the multi-armed-bandit (MAB) problem. Then, an MAB-based method is introduced to solve the formulated transmission parameter selection problem based on the acknowledgment (ACK) packet and the power consumption for data transmission of the LoRa device. The performance of the proposed method is evaluated by the constructed actual LoRa network. Experimental results show that the proposed method performs better than fixed assignment, adaptive data rate low-complexity (ADR-Lite), and e-greedy-based methods in terms of both transmission success rate and energy efficiency.

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Therefore, we investigate the performance of a QA solution to an optimization problem in wireless networks. Specifically, we aim to maximize the sum rate by jointly optimizing clustering, subchannel assignment, and power allocation in a multiautonomous aerial vehicle-aided wireless network. We formulate the sum rate maximization problem as a combinatorial optimization problem. Then, we divide it into two subproblems: 1) a QA-based clustering and 2) subchannel assignment and power allocation for a given clustering configuration. Subsequently, we obtain an optimized solution for the joint optimization problem by solving these two subproblems. For the first subproblem, we convert the problem into a simplified quadratic unconstrained binary optimization (QUBO) model. As for the second subproblem, we introduce a novel QA algorithm with optimal scaling parameters to address it. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of the sum rate and running time.

With the rapid increase in Internet of Things (IoT) devices, packet collision has become a serious problem in Long Range (LoRa) communication. Furthermore, the rising demand for mobile IoT applications necessitates parameter allocation methods that take into account the mobility of End Devices (EDs). However, the existing transmission parameter selection methods considering mobility are almost centralized designs, which may increase the probability of interference and system latency. The decentralized design is few and requires prior information on ED, which may cause scalability issues, e.g., adding ED to an existing network. To solve the problems above, a method through a fully autonomous distributed design supposing the mobility of ED using Double Deep Q Network (DDQN) is proposed in this paper. In the proposed method, ED trains the model based on ACKnowledge (ACK) packets and its location information without any prior information. Performance evaluation results show that the proposed method can achieve a higher packet delivery rate than other autonomous distributed methods in mobile IoT scenarios.

It is necessary to develop an efficient channel selection method with low power consumption to achieve high communication quality for distributed massive IoT system. To this end, Ma et al. [1] proposed an autonomous distributed channel selection method based on the Tug-of-War (ToW) dynamics. The ToW-based method can achieve equivalent performance to UCB1-tuned [2], [3] with low computational complexity and power consumption, which is recognized as a best practice technique for solving multi-armed bandit (MAB) problems. However, Ref. [1] only considered fixed IoT devices with simplex communication.

The number of IoT devices may dramatically increase in the near future. Numerous IoT devices may generate enormous traffic, which causes network congestions and packet losses. To manage network congestions, Ma et al. have proposed a channel selection algorithm based machine learning for IoT devices. They modeled channel selection as Multi-Armed Bandit problem and have designed a algorithm based on Tug-of-War dynamics to solve this problem. Furthermore, they confirmed dynamic channel selection in a local area where devices are crowded. In this paper, we conduct evaluation experimentation in real environment where devices are coexisting with other IoT systems, Sigfox and LoRaWAN. Our experimental results using our implemented systems show that each IoT node selects appropriate channel by the proposed algorithm based on reinforcement learning and the packet delivery rate (frame success rates) and fairness among the sensor nodes can be improved by the proposed scheme.

For the realization of an Internet of Things (IoT) with high densities of nodes it is necessary that wireless communication protocols are developed that offer (1) low energy consumption, (2) simplicity of encoding and decoding, (3) an asynchronous mode of communication, and (4) an effective but simple method to deal with interference between transmissions. This paper presents the implementation, experimentation, and analysis of a protocol on the MAC sublayer that is based on the encoding of information by the silent intervals between pulses. This encoding allows for few conflicts between messages that are broadcast on the same band overlapped in time. This is demonstrated experimentally, while no adverse effect is detected on transmissions from broadcasts of a different group of nodes using the same 315 MHz band but with a different code length as coding parameter. Theoretical analysis confirms that the probability of conflicts between messages is low, even if the number of nodes increases to the order of ten thousand. This protocol facilitates the implementation of IoT nodes that are restricted in terms of hardware and energy resources.

We propose a novel database management framework for data delivery services based on Store-Carry-Forward (SCF) techniques. The platform we present consists of heterogeneous wireless opportunistic networks of long-range narrowband and short-range broadband communications. We introduce a blockchain-based method by which to verify the record of delivery events on a decentralized network. A new consensus mechanism named proof-of-forwarding (PoF) is proposed to substitute the function of previously proposed proof-of-work (PoW) methods, while significantly improving computational complexities of block generation. Specifically, in our proposal a block is generated exclusively when data delivery agents perform node-to-node direct communication using a short-range high-speed wireless standard to deliver data. We additionally propose a digital signature overlay to prevent malicious nodes from producing fake transactions without any effort to carry data content to recipients. Simulation results show that our blockchain-based framework robustly manages data delivery records, where 97% of Distributed Denial-of-Service (DDoS) attacks can be prevented even when half of the entire nodes are assumed to be malicious.

We propose the development of a new measurement, assessment, and notification system of building states. This system will utilize the sensing and communication capabilities of the IoT concept. When it detects an emergency or any structural instability within the building, the system will transmit information about the current situation and provide predictions regarding potential danger.

We develop an algorithm to optimize routing and channel assignment for multi-channel wireless mesh networks. We apply our proposed algorithm to a cognitive radio system using TV white space channels, which has been developed in previous researches. To maximize the capacity and throughput of such multi-channel mesh networks, conventional algorithms separately deal with two problems, the channel assignment problem and the routing problem. Because the routing and channel assignment affect each other for maximizing the throughput, the real optimal solutions cannot be obtained by such conventional separated optimization approaches. In this paper, we combine those two problems as one optimization problem, to solve the real optimal state of the channel assignment and the routing. We formulate an objective function of the problem by using a new state variable with constraints. We apply an exact algorithm and a heuristic algorithm to our combined problem and show effectiveness of the proposed scheme by comparing their throughput performance with the conventional scheme using exact algorithms. Furthermore, we implement a wireless mesh network running our proposed algorithm using 2.4GHz wireless LAN. Our experimental results show that the proposed scheme has better performance than the conventional separated optimization even in a real system.

Cognitive radio technology improves radio resource usage by reconfiguring the wireless connection settings according to the optimum decisions, which are made on the basis of the collected context information. This paper focuses on optimization algorithms for decision making to optimize radio resource usage in heterogeneous cognitive wireless networks. For networks with centralized management, we proposed a novel optimization algorithm whose solution is guaranteed to be exactly optimal. In order to avoid an exponential increase of computational complexity in large-scale wireless networks, we model the target optimization problem as a minimum cost-flow problem and find the solution of the problem in polynomial time. For the networks with decentralized management, we propose a distributed algorithm using the distributed energy minimization dynamics of the Hopfield–Tank neural network. Our algorithm minimizes a given objective function without any centralized calculation. We derive the decision-making rule for each terminal to optimize the entire network. We demonstrate the validity of the proposed algorithms by several numerical simulations and the feasibility of the proposed schemes by designing and implementing them on experimental cognitive radio network systems.

By increase of various radio access network (RAN) services, available spectrum resources for mobile communications get decrease, and efficient use of the radio resource is becoming a very important issue. In order to optimize the radio resource usage and maxmize the throughput and quality of service (QoS), the link aggregation technologies to utilize multiple different available RANs have been studied. However, in such heterogeneous wireless networks, it is difficult to improve the throughput by their aggregation because of the differences among the QoSs of the different RANs. In this paper, we propose an autonomous parameter optimization scheme using a machine learning algorithm, which maximize the throughput of the heterogeneous RAN aggregation system. We evaluate the performance of the proposed scheme implemented on a cognitive wireless network system called Cognitive Wireless Cloud (CWC) system, connected to real wireless network services, such as HSDPA, WiMAX and W-CDMA. Our experimental results of the proposed system show that the aggregation throughput can be improved with increase of the training samples, which are collected autonomously.

In this paper, we analyze effectiveness of a new metaheuristic approach, which utilizes ideal spatio-temporal chaotic dynamics generated by Lebesgue Spectrum Filter (LSF). In the previous researches on the additive chaotic noise to the heuristic searches for combinatorial optimization problems, it has been shown that the chaotic sequences with negative autocorrelation improve the performance of asynchronously updated algorithms, such as mutually connected neural networks, asynchronous heuristic searches and so on. The effectiveness of chaos can be understood by the conventional theory of the chaotic CDMA, which showed that the cross-correlation between the sequences with negative autocorrelation becomes lowest. The spatio-temporal chaotic searching dynamics with such lowest cross-correlation has been shown effective to improve asynchronously updated combinatorial optimization algorithms. In this paper, as such asynchronously updated combinatorial optimization algorithms, we introduce the 2-exchange method, the k-opt method, the Lin-Kernighan method, and the Or-opt method, and improve the performance of them by applying the LSF. Our numerical simulation results show that the performances of all above heuristic methods could be improved by using negative autocorrelation.

We show that the wireless sensor networks can be synchronized by natural environmental signals, such as temperature, humidity and so on. The proposed synchronization scheme is based on the noise-induced phase synchronization theory; the phases of the periodical limit cycle orbits of nonlinear oscillators synchronize with each other by adding a common noise signal to the oscillators. Based on this theory, we synchronize the clocks of the wireless sensor nodes by tuning them according to the phase of their nonlinear oscillator to which natural environmental signals are added as noise. In this paper, first we analyze cross-correlation of the natural environmental signals measured by ZigBee wireless sensor nodes, which are arranged outdoor at about 20m intervals, and show that the cross- correlation among the signals sensed at different sensor nodes are around 0.8 or higher. According to this result, we analyze possibility of the noise- induced phase synchronization with changing cross- correlation between the additive noises, and clarify that the nonlinear oscillators can be synchronized in the cases that the cross-correlation becomes around 0.8 or higher. Finally, we investigate feasibility of the noise-induced phase synchronization by actual data of natural environmental signals sensed at each sensor node and show it is possible to synchronize wireless sensor nodes without any interactions or communications among them.

In heterogeneous wireless network environment, various radio access networks (RANs) are available for mobile terminals which have multiple wireless network interfaces to connect to each different RAN. However, it is very difficult to improve the throughput by aggregating different RANs, which have different quality of service (QoS) in the throughput, the delay, the packet loss, and so on. In this paper, we propose a traffic allocation control scheme using a machine learning algorithm to improve the link aggregation throughput in such heterogeneous wireless networks. We introduce the support vector machine, which has an advantage on generalization to have good performance for unknown input patterns. By our experiments using a cognitive wireless network system, called Cognitive Wireless Clouds, it is clarified that our proposed scheme improves the throughput higher than the conventional methods based on the throughput of each RAN.

This paper investigates effectiveness of distributed radio access network (RAN) selection schemes to optimize radio resource usage, for the realistic heterogeneous wireless networks, in which the distribution of the base stations are non-uniform and the non-cognitive legacy terminals also connect to the same RANs. Our proposed algorithm is based on mutually-connected and higher-order neural networks, which can directly optimize any objective functions of entire heterogeneous wireless networks, by completely distributed procedure. Our simulation results show that the proposed scheme is effective even for non-uniform heterogeneous wireless network environments, especially for higher-order and complex optimization problems. Such improvements in the proposed method also can be seen also for the cases that the legacy non-cognitive terminals co-exist in the same heterogeneous wireless networks.

We propose a distributed radio resource usage optimization algorithm to satisfy users' QoS requirements in heterogeneous wireless networks. Our optimization algorithm is based on the mutually connected neural network dynamics, which can be run distributively and autonomously, and does not require centralized computation. We define an optimization problem to satisfy QoS requirements of users' applications, and compose a neural network to solve the problem autonomously. Since the objective function of the defined problem becomes higher order function, we apply a higher order neural network model that minimizes higher order energy function by its distributed and autonomous updating. By computer simulation, the proposed method has been shown effective to optimize such a problem, without any centralized computation.

User-centric dynamic spectrum access (DSA) in heterogeneous cognitive networks is an approach which allows cognitive radio (CR) to select and utilize radio resource according to user preferences. Defining a utility function corresponding to different preferences is required for the implementation and evaluation of a user-centric DSA scheme. This paper proposes a numerical representation method of common user preferences, and a numerical representation method of user satisfaction corresponding to multiple preferences. User satisfaction is defined based on the amount of received service and the perceived fairness to the service, it is used as a utility function to evaluate the affects of a user-centric DSA scheme to network load and to the fairness of resource sharing among CRs.

We have designed and implemented a radio access network selection algorithm for heterogeneous wireless network environment, in which mobile terminals can adaptively and seamlessly handover among different radio access technologies. We propose a radio resource usage optimization algorithm without centralized computation on the network side, which is based on the Hopfield neural network dynamics whose energy function autonomously minimizes by distributed update of each neuron. We have implemented the proposed algorithm on an experimental heterogeneous wireless network system, which includes functionalities defined in IEEE 1900.4. First, we show that the proposed algorithm can optimize fairness of the throughput by numerical simulation. Then, we show that our algorithm works correctly and optimizes radio resource usage also on the experimental wireless network by distributed handover decisions on the terminal side.

Implementing dynamic spectrum assignment and access technologies in heterogeneous wireless networks may lead to considerable improvement in spectrum usage and quality of service. In this paper we evaluate the performance of SINR-based dynamic spectrum assignment and access algorithms using simulation. We compare network capacity values gained using these two algorithms with the one gained using SINR-based vertical handover algorithm. We show that dynamic spectrum assignment and access algorithms have considerable gain in network capacity compared to handover algorithm. These performance evaluation results prove the efficiency of the dynamic spectrum assignment and access technologies.

In this paper, dynamic spectrum access scenario where cognitive terminal having multi-homing capability dynamically access frequency bands assigned to different radio access networks is considered. For this scenario, QoS-guaranteed load-balancing dynamic spectrum access algorithm is proposed. For each cognitive terminal, this algorithm selects wireless access nodes to connect to. Also, it selects amount of data to transmit via each connection. Performance of the proposed dynamic spectrum access algorithm is compared to SINR-based and load-balancing vertical handover algorithms using simulation. It is shown that the proposed load-balancing dynamic spectrum access algorithm has considerable network capacity gain compared to both load-balancing and SINR-based vertical handover algorithms. Compared to load-balancing handover algorithm, this gain is equal to 10-40% depending on traffic rate per cognitive terminal.

The wireless sensor networks, which collect various data from a physical environment, are usually defined as an ad hoc network consisted of a huge number of tiny wireless sensor nodes whose computing power and battery capacity are limited. Because it is difficult to replace all of batteries on such a huge number of sensor nodes, maximization of the lifetime of the network has been one of the important research issues. To optimize such a network with a huge number of the sensor nodes, autonomous and decentralized computing and reconfiguration schemes can be considered suitable. Therefore, in this paper, we propose a routing reconfiguration method based on an autonomous optimization dynamics of the mutually connected neural network which minimizes its own energy function with autonomous and distributed computing. We show that the proposed method can optimize the routes for maximizing the lifetime of the sensor network, without any centralized computing nodes.

This paper proposes a user-centric access point selection scheme to minimize the number of unnecessary reconfigurations of cognitive terminal. In this scheme, number of terminal reconfigurations is defined based on the dwell-time of terminal which is estimated from user’s mobility and coverage area of the access point (AP). Each terminal selects one AP from a candidate list that is generated based on user’s preferences. Information needed for the selection scheme is obtained from both network-side and terminal-side entities. Four different algorithms are investigated in order to find the best combination of dwell-time and other parameter such as load of APs, to minimize the number of reconfigurations and to achieve load balancing for Radio Access Networks simultaneously.

We propose an autonomous and decentralized optimization method for heterogeneous wireless networks, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. For optimum selection of radio resources, we introduce dynamics of the mutually connected neural network which converges to an optimal state since its property is to autonomously decrease its energy function. In this paper, we apply such neurodynamics to user-centric optimum radio access selection, which maximizes the total throughput of the entire networks, and minimizes the power consumption of the user terminal and the communication cost. We compose a neural network that solves such a problem, and we show that they can be optimized by distributed decisions based on autonomous and simple neuron updates on the terminal side.

This paper introduces the concept, features and architecture of a software defined cognitive radio system: Cognitive Wireless Clouds (CWC) that can realize user-centric and scalable network based on unique cognitive spectrum access, cross-network signaling, network optimization, and fast reconfiguration methods. Then, this paper shows results of feasibility studies on a software defined cognitive radio (SDCR) terminal that can access to the CWC. This includes the configuration of the SDCR terminal and a measurement data for spectrum sensing period and reconfiguration period by using software packages of W-CDMA and IEEE802.11a.

Cognitive wireless ad hoc is highly focused as a promising future wireless technology. However, cognitive wireless ad hoc networks have some difficulties of neighbor discovery and routing. For helping neighbor discovery and routing in the cognitive wireless ad hoc networks, we introduce a common link control radio (CLCR) that is a common active wireless system of cognitive terminals. In addition, we propose a novel cognitive ad hoc routing protocol based on a common link control radio (CLCR) called a minimum weight routing protocol. We show that the proposed routing protocol can greatly reduce communication overhead in cognitive wireless ad hoc networks.

We propose a neurodynamical approach to a large scale optimization problem in the cognitive wireless clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. In order to deal with such a cognitive radio network, the game theory has been applied in order to analyze stability of the dynamical systems consisting of the mobile terminals' distributed behaviours, but it is not based on fundamental optimization property. As more natural optimization dynamical system model suitable for large-scale complex systems, we introduce the mutual connection neural network dynamics which converges to an optimal state with always decreasing property of its energy function. In this paper, we apply such a neurodynamics to optimization problem in radio access technology selection. We composed a neural network which solves the problems, and showed that it is possible to improve total average throughput only by distributed and autonomous neuron updates on the terminal side.

We propose a fast and efficient end-to-end QoS measurement scheme. It collects distributed QoS information which is autonomously measured at each network segment in the backbone networks, and estimates the end-to-end QoS parameters from the collected data. It does not require sending the probe packets and statistical analysis for some duration, the proposed method provides the end-to-end QoS information much faster than the conventional methods. The measurement time of our method depends on round-trip time of the end-to-end path which is typically on the order of 101 or 102 milliseconds. In the user-centric heterogeneous wireless networking, each mobile terminal has to autonomously select an optimal PoAs (point of attachment; access point and base station) from available network candidates according to such an end-to-end QoS information to continue the services (VoIP, video conference, Web browse, etc.) since their QoS requirements are different. Therefore, the fast end-to-end QoS estimation is crucial for seamless handover among various networks with a right decision. In this paper, we have shown that our proposed method can estimate end-to-end delay and jitter with only a few milliseconds error by simulation.

This paper describes the design and implementation of a ubiquitous networking server that enables cross-device as well as cross-network mobility by using an out-of-band signaling method with a location sensing system for context-aware seamless network services. The context includes the user's presence, position of the user and neighboring devices, network interfaces attached to the mobile terminal, availability and available bandwidth of each network, priority in selecting networks, capabilities of the mobile terminal and devices, and installed applications. The server is designed to be placed not in operators' networks but in users' local networks such as home and enterprise networks to provide users with mobile IP-based handover capability between radio networks in a distributed manner. This would accelerate the introduction of Mobile IP to network services, offering flexibility in creating human-centric network services. The out-of-band signaling method we propose exchanges information between mobile terminals and the network about user and device authentication, session initiation protocol (SIP) signaling, mobile IP signaling, the position of users and devices, and signaling for cross-device mobility. These features provide us unprecedented flexibility in communication

We have designed and implemented a compact and simple network oriented device, a NetSpeaker, which mainly consists of a DA converter, an amplifier and buffers, in order to enable setting many terminals and realize ubiquitous computing environment. It receives WAV formatted data via IP packets and simply plays it by DA converting. All the sound outputs of Windows XP/2000 applications can be played on remote NetSpeakers, since we implemented the sound forwarding function as a virtual sound device, which generates WAV data of sound outputs of applications and sends them to a selected NetSpeaker. In this paper, we show design of this NetSpeaker system and its application in a service mobility environment.

An out-of-band basic access signaling (BAS) protocol is introduced for use in heterogeneous wireless networks. The BAS protocol enables a mobile terminal to communicate with an agent in a fixed network through any radio access network (RAN) chosen by the user as the basic access network (BAN). The agent uses the information on the location of mobile terminals and on the availability of RANs to decide which RAN to use, taking the user's preferences into account. This protocol was implemented in a demonstration system using existing radio systems and the standard mobile IP protocol. The separation of the signaling path from the data paths was found to save energy and to facilitate handover between RANs.

A context-based adaptive communication system is introduced for use in heterogeneous networks. The context includes the user's presence, location, available network interfaces, network availability, network priority, communication status, terminal features, and installed applications. An experimental system was developed to clarify the feasibility of using context information to flexibly control a network and applications. The system operates on a seamless networking platform we developed for heterogeneous networks. By using contexts, the system can show the caller and its callee the applications they can access that are available through the network before communication occurs. Changes of the contexts can switch the on-going application to another during communication. These features provide us with unprecedented styles of communications.

For IP-based always-on type multi-service user terminals, which may have multiple wireless interfaces, energy-efficiency is a key requirement. It is crucial to take into account the power consumption behavior and delay patterns associated with each state and transitions of the wireless devices to formulate efficient operation of multi-module terminals, especially in services where media/session handover between wireless networks are targeted. This paper investigates the power consumption pattern or behavior of some selected wireless interfaces that are good candidates for being part of the future of the multi-service user terminals. We propose a simple model for predicting energy consumption in a terminal attributed to the wireless network interfaces. We measured the actual consumption pattern to estimate the parameters of the model.

A key word describing next-generation wireless communications, so called 4G, is "seamless". As part of the e-Japan plan promoted by the Japanese government, the MIRAI (multimedia integrated network by radio access innovation) project is aiming at the development of new technologies that will enable seamless integration of various wireless access systems for practical use by 2005. First, this paper describes heterogeneous wireless networks and introduces the concept of MIRAI. The most significant feature of MIRAI is the provision of a set of signaling functions: radio-access-network discovery and selection, heterogeneous paging, and vertical handoff. These functions make it possible to offer seamless services between different wireless systems. A medium access control (MAC) protocol for basic access networks, which are dedicated wireless systems providing the signaling, is then introduced. A proof-of-concept demonstration system is also briefly introduced. The delay performances of the signaling procedures are presented.

A key word describing next-generation wireless communications, so called 4G, is "seamless." As part of the e-Japan Plan promoted by the Japanese government, the MIRAI (Multimedia Integrated network by Radio Access Innovation) project is aiming at the development of new technologies that will enable seamless integration of various wireless access systems for practical use by 2005. First, this paper describes heterogeneous wireless networks and introduces the concept of MIRAI. The most significant feature of MIRAI is the provision of a set of signaling functions: radio-access-network discovery and selection, heterogeneous paging, and vertical handoff. These functions make it possible to offer seamless services between different wireless systems. A medium access control (MAC) protocol for basic access networks, which are dedicated wireless systems providing the signaling, is then introduced. A proof-of-concept demonstration system is also briefly introduced. The delay performances of the signaling procedures are presented.

A novel architecture for future integrated wireless network is proposed in MIRAI (Multimedia Integrated network by Radio Access Innovation) where a dedicated network takes care of the mobility functionalities of the participant wireless networks in the integration. The dedicated network, called basic access network (BAN) is used for paging whenever a call arrives for the mobile user for any of the wireless interfaces of the user's multiservice terminal. We analyze a scheme for using the BAN channel, and associated network to aid the AAA (authentication, authorization and accounting) and IP mobility functionalities of the integrated network environment in a seamless fashion.