As global logistics demand increases, improving the efficiency of warehouse operations has become critical. To achieve this, it's crucial to identify inefficient tasks and layouts by recognizing various warehouse conditions. To address these challenges, we've constructed a large-scale camera infrastructure to convert object positions and movements into data. However, transmitting all video data to the cloud results in significant data transmission and power consumption. Edge AI cameras analyze and extract video data locally, transmitting only essential information and significantly reducing them. Edge AI cameras require a low-computation, high-accuracy object detection model due to limited computational power and complex warehouse environments. Furthermore, the same object appears differently based on the camera's position and angle. Therefore, customizing models for each camera improves accuracy, but the annotation cost would be very high. In this study, we propose a method to perform part of the training data generation on the camera, reducing data transmission and improving annotation efficiency.

As we know, the optimization of large-scale and complex wireless networks is a fundamental research problem. However, deriving an optimal solution is challenging due to the following two facts; (i) the solution space is dependent of the scale of the wireless environment, namely the number of terminals and that of base station (BS) candidates, and (ii) when considering a dynamic environment that changes over time rather than a static one, the solution space grows proportionally to the time scale. One promising approach to the acceleration in searching an optimal solution is to reduce the solution space. To this end, we propose a graph-based reduction approach for dynamic wireless environments. Namely, we represent the dynamic wireless environment as a time-series graph and compress it. The key idea of our proposal is twofold. Firstly, we describe the relationship between terminals and BS candidates as a bipartite graph. Secondly, we reduce the time-series graph in temporal and spatial dimensions by iteratively merging graph pairs and node pairs. As an application of our graph compression, we solve a BS placement problem, which is a well-known optimization problem, using an extended greedy algorithm. Moreover, we discuss the effect of the graph compression on the solution quality through experiments.

Real-time and reliable wireless communication is increasingly vital for applications like remote robot control. To support such needs, radio environment maps (REMs) showing metrics like signal strength at each location in the area have gained attention. However, their values are affected by noise, fading, and positioning errors, so representing only the mean is insufficient; their full probability distribution is needed. While Gaussian Process Regression (GPR) is commonly used to build REMs, it often struggles to capture distribution shapes and is inefficient at scale. To address these challenges, we build on Probabilistic Feature Networks using Prior-Data Fitted Networks (PFNs), which utilize a transformer trained on GPR, and propose the Mixture of PFNs (MoPFNs) to model the probability distribution as a mixture of Gaussian distributions. Experimental results in a 28 GHz millimeter wave (mmWave) environment show that the proposed method reduces the average error in estimated mean RSRP to less than 10 dB, accurately captures variations in distribution shape across locations, and improves execution time by approximately 92.5%.

The rapid growth of e-commerce, rising consumer expectations, and labor shortages in Japan pose challenges for logistics warehouse optimization. While automation has advanced outbound operations, inbound processes remain inefficient. This study presents a comprehensive approach to digitizing and optimizing large-scale logistics warehouses, with a case study at TRUSCO NAKAYAMA Corp. in partnership with Nagoya University. Key technologies include a large-scale camera array, multi-camera object tracking, and smartphone-based task estimation. Our contributions include real-time tracking of personnel and packages, cooperative annotation for improved object recognition, and synthetic data augmentation. Additionally, truck berth analysis and indoor localization enhance operational efficiency. To optimize worker shifts and warehouse layout, we apply Factorization Machine Quantum Annealing (FMQA), achieving a 37.4% reduction in lead times and a 14.3% decrease in labor hours. A visualization tool enables warehouse operators to make data-driven decisions. This research demonstrates the potential of digital transformation in logistics and provides a scalable framework for broader industry adoption.

As global demand for logistics continues to grow, improving the efficiency of warehouse operations has become increasingly important. While many processes in logistics warehouses have been automated, the receiving area still relies heavily on manual work. Therefore, optimizing this area is essential. When optimizing operations in a logistics warehouse, various problems must be considered, such as layout design and worker assignment. Previous research has typically focused on these problems individually. However, because they are closely related, jointly optimizing them can further improve overall efficiency. In this study, we focus on the receiving area and conduct joint optimization of layout design and worker assignment. Specifically, we extend and apply a black-box optimization method called FMQA, which combines Factorization Machines (FM) with Quantum Annealing (QA). By using Factorization Machine regression, we build an objective function from simulator data. We then use Quantum Annealing to minimize it and improve the receiving area. A comparison with an actual warehouse environment, using the multi-agent simulator, shows that our approach can reduce mean package processing time by up to 22.1%. This result demonstrates the effectiveness of joint optimization of layout design and worker assignment.

To enable robust wireless communications towards the future Cyber Physical System by optimizing beamforming in real time, it is essential to quickly estimate changes in the radio environment map (REM) after altering the beam direction. In addition, because of the difficulty to predict detailed REMs with high accuracy, especially for millimeter waves, a probabilistic REM, which estimates the radio signal strength at any point as a probability distribution rather than as a deterministic value, is expected. In this paper, to enable fast and probabilistic REM estimation, we propose a scheme that divides the estimation formula into a linear term and a Gaussian Process Regression (GPR) term to minimize the recalculation time associated with beam adjustments. We also propose a GPR kernel that uses polar coordinates centered on the base station angle to better capture the characteristics of narrow beams like millimeter waves. We evaluated the proposed scheme using Kullback-Leibler Divergence (KLD) to compare the measured and estimated distributions of the radio signal strength. In the REM estimation after beam adjustment, an accurate distribution is estimated at 29 out of 35 points with a KLD of 0.5 or lower. The proposed method achieved approximately 50 times faster REM reconstruction compared to a simple REM using GPR, enabling dynamic beam-forming optimization in mm-wave environment.

As global demand for logistics continues to grow, optimizing the automation and efficiency of distribution warehouse operations is of paramount importance. Digitalizing warehouse environments, which refers to the process of sensing the physical space and extracting meaningful information from the obtained data, offers a promising solution to this challenge. However, converting raw warehouse data, such as video footage captured inside the warehouse, into actionable metadata (e.g., tracking the movement paths of workers and products or analyzing the usage patterns of different warehouse locations) often necessitates significant human intervention. The rise of machine learning further complicates this, as it requires the manual preparation of extensive training datasets. In this paper, we introduce a framework that semi-automates the digitalization process in complex warehouse settings. This framework employs dense optical flow and representation learning to autonomously segment warehouse objects and cluster similar objects, thereby substantially cutting down on annotation costs. To evaluate our approach, we constructed a large-scale data collection platform with over 60 fixed cameras in a real-world logistics warehouse, and the video data from this platform was then applied to our framework. Our evaluations indicate that our method markedly reduces both the time and resources required for warehouse digitalization using the captured video data.

The IEEE 802.11ax amendment defines Spatial Reuse (SR) operation, enabling parallel transmissions in overlapping basic service set (OBSS) scenarios. Adjusting the clear channel assessment (CCA) threshold of the transmitting node for the OBSS signal, assuming that the SINR of the received frame is sufficient, improves the throughput by expanding the transmission opportunities and avoids exposed node problems. However, depending on node deployment, SR makes it impossible to ensure a high signal-to-interference-to-noise ratio (SINR). This is a significant problem, especially for communications requiring high-quality of service.To address this problem, we propose a method to ensure a high SINR for high-priority frames (QoS frames) when SR is applied. First, we label "QoS Color" on individual frames to indicate the priority level (QoS/best-effort). Then the PHY header indicates whether the frame is a QoS frame. The receiver node references this information and dynamically sets the CCA threshold to lower the threshold for protection in the case of a QoS frame. Second, before transmission, both the CCA threshold and the transmission power are higher for QoS frame transmission than for best-effort frame transmission. This ensures the opportunity to transmit QoS frames. We describe a simple indicator for the above control to change the CCA threshold according to the required throughput of QoS frames. We also confirm the effectiveness of the proposed method by computer simulation.

Human-robot collaboration and cooperation are critical for Autonomous Mobile Robots (AMRs) in order to use them in indoor environments, such as offices, hospitals, libraries, schools, factories, and warehouses. Since a long transition period might be required to fully automate such facilities, we have to deploy AMRs while improving safety in the mixed environments of human and mobile robots. In addition, human behaviors in such environments might be difficult to predict. In this paper, we present a Digital Twin for Autonomous Mobile Robots system named DigiMobot to support, manage, monitor, and validate AMRs in indoor environments. First, DigiMobot can simulate human behaviors and robot movements to verify and validate AMRs to improve safety in a virtual world. Secondly, DigiMobot can monitor and manage AMRs in the physical world by collecting sensor data from each robot in real-time. Since DigiMobot enables us to test the robot systems in the virtual world, we can deploy and implement AMRs in each facility without any modifications. To show the feasibility of DigiMobot, we develop a software framework and two different types of autonomous mobile robots. Finally, we conduct real-world experiments in a warehouse located in Saitama, Japan, in which more than 400, 000 items are stored for commercial purposes.

Interaction between filovirus glycoprotein (GP) and the Niemann-Pick C1 (NPC1) protein is essential for membrane fusion during virus entry.Some single-nucleotide polymorphism (SNPs) in two surface-exposed loops of NPC1 are known to reduce viral infectivity.However, the dependence of differences in entry efficiency on SNPs remains unclear.Using vesicular stomatitis virus pseudotyped with Ebola and Marburg virus GPs, we investigated the cell-to-cell spread of viruses in cultured cells expressing NPC1 or SNP derivatives.Eclipse and virus-producing phases were assessed by in vitro infection experiments, and we developed a mathematical model describing spatial-temporal virus spread.This mathematical model fit the plaque radius data well from day 2 to day 6.Based on the estimated parameters, we found that SNPs causing the P424A and D508N substitutions in NPC1 most effectively reduced the entry efficiency of Ebola and Marburg viruses, respectively.Our novel approach could be broadly applied to other virus plaque assays. Author summaryEbola (EBOV) and Marburg (MARV) viruses, which are included viruses of the family Filoviridae, cause severe hemorrhagic fever in humans.Filovirus particles is adsorbed to the cell through glycoprotein (GP), which is the only viral surface protein.Interaction between the filovirus sugar protein (GP) and the Niemann-Pick C1 (NPC1) protein plays a key role in membrane fusion during virus entry.Although some single-nucleotide polymorphism (SNPs) in two surface-exposed loops of NPC1 are known to reduce viral infectivity, the dependence of differences in entry efficiency on SNPs has not been studied.We therefore investigated the cell-to-cell spread of viruses in cultured cells expressing NPC1 or SNP derivatives.Using a mathematical model describing spatial-temporal virus spread, we quantitatively analyze viral entry efficiency and how this affected cell-to-cell spread.

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.

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.

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.

This paper focuses on storage and reproduction systems for environmental haptic sensations. A large force vibration is caused by the differential of viscosity momentum in the reproduction system based on force control. To solve this problem, the reproduction system based on momentum control has been proposed in previous our research. The improved reproduction system has a configuration that the momentum control loop is added to the force control loop. The high-stiffness environment in which the actuator does not move cannot be reproduced because the system generates the force command that correspond to the position of the actuator. Hence, this paper newly proposes storage and reproduction systems based on position control considering the environmental mechanical admittance. The proposed system is able to reproduce the environmental information without additional control loop. The effectiveness of the proposed method is confirmed by experiments.

The approach used to implement mechanical impedance can be generally separated into the following two types: an impedance control system and an admittance control system. When an impedance control system reproduces a hard environment, large vibrations are generated. In contrast, an admittance control system is excellent at reproducing a high-stiffness environment for utilizing the inverse of the environmental impedance. However, stability analyses have not been conducted on admittance control systems. Therefore, in this study, a stability analysis for an implementation system of mechanical impedance is conducted using a pole placement. The analysis results indicate that the pole placement of the impedance control system is moved toward the direction of an imaginary axis based on the influence of the environmental stiffness. Hence, large vibrations occur when the environmental stiffness is determined to be a large value. On the other hand, for the admittance control, when the environmental stiffness is dominant, pole placement is not affected by the environmental stiffness. The superiority of the admittance control system in a high-stiffness environment was shown through an experiment.

The first giga-bit class wireless local area network (wireless LAN) below 6 GHz frequency (the IEEE 802.11ac standard) is attracting attention due to its capability of handling much more of the rapidly growing data traffic. The increased density of wireless LAN access points (APs), however, has induced inter-cell interference that severely degrades system performance. This problem seems to be getting worse with the increase of 802.11n- and 802.11ac-based APs, which establish basic service sets (BSSs) with wider channel bandwidths. In order to mitigate this problem, we propose an efficient radio resource allocation scheme called RATOP that can be applied to a managed wireless LAN system with a central coordinator. On the basis of channel monitoring results, capability, and data traffic information obtained from APs, the central coordinator computes the quasioptimal frequency channel and channel bandwidth of each AP in such a way that the given utility function is maximized. Numerous simulations with UDP traffic flows on the coexistence scenarios of 802.11a/n/ac show that the RATOP works well and reduces the number of overlapping BSSs by 65%-89%. As a result, the RATOP improves the minimum throughput and fairness among throughput of APs even when there exist foreign APs of other network domains that cannot be controlled by the coordinator.

The upcoming wireless LAN standard IEEE 802.11ac extends the channel bandwidth from conventional 20 or 40 MHz to 80 or 160 MHz to increase the data rate. Consequently, there could be stations supporting different channel bandwidths in one area. Stations belonging to the same basic service set (BSS) have to operate on the primary channel, which is a common 20 MHz sub-channel. In such a situation, the secondary channels associated with the primary channel may remain unused and, as a result, the system capacity will be degraded. To address this problem, we propose a multi-user multi-channel transmission technique to improve the efficiency of downlink transmissions. The computer simulation results confirm the effectiveness of the proposed method.

In this paper, we propose a channel estimation scheme on amplify-and-forward (AF) cooperative orthogonal frequency division multiplexing (OFDM)-based wireless local area network (LAN) systems with one relay. The proposed scheme exploits an Hadamard matrix-based training structure and a maximum ratio combining (MRC)-based estimator for received training signals in two subslots of relaying transmission. The scheme improves estimation accuracy in spite of almost no increase of complexity. We analyze normalized mean square errors (NMSEs) of the scheme and derive its analytical expressions for Rayleigh fading channels. We simulate the NMSE and packet error rate (PER) performance of the conventional and proposed schemes in typical relay location scenarios. The simulation results show that the proposed scheme has better performance than the conventional one in each of the scenarios.

Multiuser MIMO (MU-MIMO) transmission offers higher channel capacity than SISO transmission in practical system. However, MU-MIMO channel capacity in multi-cell environment is limited by inter-cell interference (ICI). Distributed zero forcing beamforming (DZFBF) is one of candidates to improve MU-MIMO channel capacity by mitigating ICI. To evaluate the performance and feasibility of DZFBF, we have developed a real-time testbed implemented on a FPGA. For this, we also implement a simple weight generation process that uses feedback channel state information from STAs; it is a extension of the weight generation from MU-MIMO systems. Moreover, we demonstrate the real-time transmission performance of our testbed in an actual indoor multi-cell environment. The experimental results indicate that the effectiveness of DZFBF is twice as high as that of MU-MIMO transmission with TDMA.

Cooperative communication can be regarded as virtual multiple-input multiple-output (MIMO) systems where distributed multiple terminals behave as a virtual antenna array. This paper presents two space-time coding (STC) methods, i.e., the Golden code and Alamouti code, for cooperative orthogonal frequency division multiplexing (OFDM)-based wireless local area network (LAN) systems. We propose a decoding scheme for both STC methods. The proposed decoding scheme for the Golden code has approximate maximum likelihood (ML) property, and the one for the Alamouti code has exact ML property. we show the performances of the two proposed schemes for various typical relay locations through simulations. Simulation results reveal that energy gain greater than 2.1 dB can be obtained with the proposed schemes over non-cooperative (direct) communication in each relay location at a packet error rate of 0.1.

This paper presents indoor experimental results on an implemented real-time downlink multiuser multiple-input multiple-output (DL-MU-MIMO) transceiver with a channel state information (CSI) feedback scheme for next generation wireless LAN systems. Estimated CSI is used at the access point (AP) transmitter to calculate transmit beamforming weight for DL-MU-MIMO transmission. A CSI compression scheme using discrete cosine transform (DCT) is proposed to reduce CSI feedback overhead. The transceiver performance is demonstrated and indoor experiments clarify that it is a feasible tool for real-time signal processing that improves system throughput over that of transceiver without a CSI compression scheme with simple digital signal processing of single-antenna stations (STAs).

Multiuser (MU) - multiple input multiple output (MIMO) techniques are attractive for increasing the downlink spectrum efficiency while requiring few antennas at each user station (STA). As practical precoding methods for multi-user MIMO systems, block diagonalization (BD) and successive optimization (SO) algorithms have been investigated using the metric of channel capacity. However, the throughput of SO algorithms has not been evaluated for wireless LAN systems. This paper focuses on the two-STA scenario where a close STA lies in the very near field of the access point (AP) while the distant STA lies farther from the AP; it proposes MU-MIMO transmission based on an SO algorithm that uses only the channel state information (CSI) of the close STA. The proposed method determines the transmission weight for the distant STA so as to nullify the close STA and reduces the inter-user interference at the distant STA by decreasing the transmission power for the close STA. Thus, the distant STA does not have to implement the CSI feedback function. Simulations compare zero forcing (ZF), BD, and proposed SO algorithms and confirm the conditions wherein the proposed method outperforms single user MIMO and multiuser MIMO based on ZF and BD algorithms.

This paper presents indoor experimental results on an implemented real-time downlink multiuser multiple-input multiple-output (DL-MU-MIMO) transceiver for next generation wireless LAN systems. In the transceiver, channel state information (CSI) is estimated at each station (STA) and fed back to an access point (AP) on a real-time basis. At the AP, transmit beamforming weight for DL-MU-MIMO transmission is calculated by using the estimated CSI. This paper also proposes a fast computation scheme for computing transmit weight in real time. Indoor experiments with the DL-MU-MIMO transceiver demonstrate its feasibility. The experimental results clarify that DL-MU-MIMO transmission can achieve system throughput of greater than 500 Mbit/s with simple digital signal processing of single-antenna STAs in an indoor environment.

In multiple antenna systems employing spatial multiplexing to raise data rates, it is preferable to use maximum likelihood (ML) detection to fully benefit from multiplexing and diversity gain. In this paper, we present a new tree search based detector that uses layer ordering in conjunction with a partial lattice basis reduction. Compared with conventional tree detectors, the error performance of the new detector closely approximates that of the optimal ML detector while achieving a dramatic reduction in complexity.

Network coding has been referred to as the way to improve the network throughput of multi-hop communication network systems. Therefore a lot of researches have focused on IP and MAC layers. However, this report focuses on the demodulation scheme for the physical layer network coding and propose a packet combining demodulation scheme for network coded multi-hop wireless systems. With this scheme, a system can receive the benefits of both cooperative transmission and network coding, and the communication quality is drastically improved. Numerical results show improvements in the packet error probability of the network coded system based on the IEEE 802.11a standard.

In multiple antenna systems using spatial multiplexing to raise data rates, it is preferable to use maximum likelihood (ML) detection to fully benefit from multiplexing and diversity gain. In this paper, we present two tree based near ML detectors that use new ordering criteria in conjunction with an efficient tree search strategy. Compared with conventional tree detectors, the error performance of the new detectors closely approximates that of the exact ML detector while achieving a dramatic reduction in complexity. Moreover, the new schemes ensure a fixed detection delay and parallelization in tree search.

In a spatial multiplexing system with multiple antennas on both sides of a wireless link, it is preferable to adopt the maximum likelihood (ML) detection to fully extract both multiplexing and diversity gain. In this paper, we present a new tree search based near ML detector that applies a new ordering in conjunction with efficient node exploration strategy. The error performance of the new detector closely approaches that of the ML detector. However, it is far less complex than conventional suboptimal tree detectors and maintains a fixed detection delay.

Recent theoretical and experimental studies indicate that spatial multiplexing (SM) systems have enormous potential for increasing the capacity of corresponding multiple input multiple output (MIMO) channels in rich scattering environments. In this paper, we describe two fast detection algorithms for the standard BLAST receiver. The first algorithm covers the nulling and cancellation (NC) detection interface that applies inflation and deflation recursion to the initialization and iteration stages, respectively. The second algorithm utilizes the QR decomposition (QRD) detection interface that reuses Cholesky decomposition and back substitution, leading to an alternative low complexity detector. Compared with the conventional fastest method, our new algorithms reduce the complexity of the preprocessing by a factor of about 2. Furthermore, they are also implementation friendly by avoiding the recursive permutation and permitting the function reuse.

This paper describes a demodulation scheme employing packet combining for packet-based cooperative multi-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM). hi this paper, we consider the simplified cooperative MIMO-OFDM transmission model based on a two-hop relay network. The proposed demodulation scheme combines the packet signals of different transmit periods. After combining, we employ maximum likelihood detection (MLD) to separate the multiplexed sub-streams. We consider the L = 2 case: i.e. the first transmit period and the second transmit period are used. Because the source-terminal and relay-terminal are located in different positions, the proposed scheme utilizes the difference between the MIMO channel matrix of the first transmit period and that of the second transmit period. As a basic performance comparison, and assuming ideal link performance between source-terminal and relay-terminal and error-free relay processing, we describe a packet error rate (PER) performance comparison. The results confirm that the proposed packet-combining scheme yields relay transmission with an approximately 1.1 dB improvement in Eb/N0over direct transmission without the packet combining scheme.

Recent studies indicate that spatial multiplexing (SM) systems have enormous potential for increasing the capacity of multiple input multiple output (MIMO) channels in rich scattering environments. In this paper, we present two efficient decision feedback equalization (DFE) algorithms for signal detection of SM scheme. The basic idea behind the new proposals is to reduce the complexity by separating the order search and filter generation. Compared with the conventional DFE detectors with optimal and suboptimal ordering rules, the new algorithms are less complex and achieve error performance that closely approximates that of the standard Bell Labs layered space time (BLAST) algorithm

This paper describes the performance of transmit antenna selection for an eigenbeam multiple-input multiple-output with orthogonal frequency division multiplexing (MIMO-OFDM) system as measured in a testbed implemented with field programmable gate array (FPGA); it also targets wireless LANs. For this verification, we employ the determinant-based transmit antenna selection approach based on the estimated instantaneous MIMO channel matrix to reduce the computational complexity. The measured packet error rate (PER) performance confirms that the eigenbeam scheme with three-select-two antenna selection scheme provides a slight degradation in required carrier-to-noise power ratio (C/N), approximately 0.2 dB from the eigenbeam-only scheme, but with significantly lower computational complexity. Furthermore, with regard to the impact of Doppler frequency, we focus on the transmission interval between MIMO channel estimation and data transmission. To suppress the degradation in required C/N to within 1.5 dB, a transmission interval of approximately 15.0 ms is permitted without using the re-estimation procedure in fd= 5 Hz, for eigenbeam 16QAM/MIMO-OFDM with three-select-two antenna selection

This paper proposes a simplified decision-feedback channel tracking scheme for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems designed for wireless LANs. In the proposed scheme, the channel matrix for each subcarrier is tentatively estimated from a replica matrix of the transmitted signal. A number of the estimated channel matrices, each determined for a different timing, are combined, and the previously estimated channel matrices are replaced by the latest ones. The packet error rate (PER) performances for the proposed scheme are evaluated by computer simulations. Assuming 5 GHz band operation, the proposed scheme keeps the PER degradation at PER=10-2to less than 1 dB when the velocity of transmitter is less than 11.8 km/h. The result represents the proposed channel tracking scheme provides sufficient mobility for wireless LAN systems. We also evaluate the computation complexity of the proposed scheme. The proposed scheme requires less than 1% of the complex multipliers needed by the conventional channel tracking scheme based on Kalman filters.

Recent studies indicate that spatial multiplexing (SM) scheme has enormous potential for increasing the capacity of MIMO channels in rich scattering environments. In this paper, we present a new MMSE decision feedback equalization algorithm for signal detection in SM systems. The new algorithm separates the order search and the filter generation to avoid recursive ordering operations. Compared to the earlier MMSE-DFE detectors with optimal and suboptimal ordering rules, the new algorithm is less complex and achieves a bit error rate performance that closely approximates that of the standard BLAST algorithm.

In this paper, we propose a new algorithm for zero-forcing decision feedback equalization (ZF-DFE) in multiple input multiple output flat Rayleigh fading channel. The basic idea is to separates the layer ordering and the filter construction operations, thus making both operations tractable and efficient. The new layer ordering strategy approximates the optimal ordering rule (VBLAST) from a geometrical perspective, and the filters in proposed ZF-DFE are constructed using a matrix Cholesky decomposition aided QR decomposition. Compared to the conventional ZF-DFE detectors utilizing optimal and suboptimal ordering rules (SQRD), the proposal is complexity efficient. Additionally, the bit error rate performance of the new algorithm significantly approximates that of the optimal ordering ZF-DFE. Thus a practical signal detection scheme for the vector channel is found.

This paper describes the experimental performance of eigenbeam multi-input multi-output with orthogonal frequency division multiplexing (MIMO-OFDM) systems as measured in a testbed implemented with field programmable gate arrays (FPGA). The testbed offers 1/5-scale real time signal processing. Extensive experiments on the testbed confirm the basic operation and performance of eigenbeam MIMO-OFDM with 16QAM. From the PER performance, we confirm that the eigenbeam scheme with permutation matrix and three transmit antennas (Mt= 3) drastically improves the required carrier-to-noise power ratio (CNR) by approximately 5.6 dB over the scheme without eigenbeam with Mt= 2. Furthermore, to determine the impact of Doppler frequency, we focus on the transmission interval between the MIMO channel estimation and data transmission. To suppress the required CNR degradation to within 1.5 dB, it is found that the eigenbeam scheme with permutation matrix and Mt= 3 permits a transmission interval of approximately 15.5 ms when fd= 5 Hz

This paper describes an SDM-COFDM (space division multiplexed-coded orthogonal frequency division multiplexing) prototype employing a hierarchical ICI (inter-channel interference) canceller for MIMO (multi-input multi-output)-based broadband wireless access systems. The prototype has two types of ICI cancellers: multiplicative and subtractive. The multiplicative canceller multiplies the received signals by the estimated inverse propagation coefficient matrix. In addition, the subtractive one generates ICI replicas, subtracts the replicas from the received symbols, weights the subtracted signals by the estimated propagation coefficients, and combines them to obtain diversity gain. Experimental results show that the newly developed prototype with 2/spl times/2 antennas obtains a 1.4 dB improvement in required CNR at the PER of 0.1 compared to that achieved with only the multiplicative canceller. It achieves more than 100 Mbit/s per 18 MHz bandwidth by using 64QAM with the coding rate of 3/4 in frequency selective fading environments.

This paper introduces a novel SDM-COFDM (space division multiplexed-coded orthogonal frequency division multiplexing) prototype for MIMO (multi-input multi-output) channel-based broadband wireless access systems. The developed prototype reduces the inter-channel interference in the SDM transmission by suing a simple feed-forward compensator that simply multiplies the received data symbols by the estimated inverse propagation coefficient matrix at each OFDM subcarrier. Moreover, in order to improve the accuracy of estimating the inverse propagation coefficient matrix, the STC (space-time coded) preamble and the common AFC (automatic frequency control) circuit have been implemented. Furthermore, the likelihood weighting, based on SNR (signal to noise power ratio) of each OFDM subcarrier, is used to improve the error correction performance of soft decision Viterbi decoding. Experimental results show that the prototype with 2/spl times/2 antennas achieves double the transmission bit rate without expanding the occupied frequency bandwidth, while achieving SISO (single-input single-output) transmission quality in fading environments. The prototype offers 108 Mbit/s per 20 MHz by using 64 QAM with the coding rate of 3/4.

For multiprocessor architecture (MPA) software radios, allocating the functions to the processors appropriately and controlling multiple interruptions between the processors are important issues in achieving real-time processing ability. This paper describes the design and evaluation of Japanese Personal Handy-phone System (PHS) software implemented in an MPA software radio platform for the realization of full-duplex real-time communication. A novel API between the CPU and DSP programs is shown that achieves real-time performance. Average DSP load and processing delay characteristics of the PHS software are measured; the results show that real-time communication was successfully achieved.

This paper proposes space division multiplexed coded orthogonal frequency division multiplexing (SDM-COFDM) scheme using inter-channel interference canceller for multi-input multi-output (MIMO) channels. It reduces the degradation due to inter-channel interference in SDM transmission with a feed-forward canceller multiplying the received symbols by the estimated propagation inverse matrix for each OFDM subcarrier. Moreover, the proposed likelihood-weighting scheme, which is based on each subcarrier's signal-to-noise power ratio (SNR), enhances the error correction performance or soft decision Viterbi decoding. Computer simulation shows that the SDM-COFDM scheme achieves more than 100 Mbit/s per 20 MHz in frequency selective fading environments with the same carrier-to-noise ratio (CNR) as the single channel COFDM.