In the 6th generation (6G), demand for high-capacity wireless communication systems is growing due to the emergence of a wide variety of applications. Small-cell use with millimeter waves and higher frequency bands is key to increasing system capacity. To achieve a large number of small-cell installations, a flexible backhaul is required to support the foundation of high-speed 6G accesses. The orbital angular momentum (OAM) multiplexing technique is effective for achieving high-capacity wireless backhauls. This paper presents the field experimental results of real-time wireless transmission using a combination of OAM and polarization multiplexing in the E-band, from 71 to 86 GHz, using an OAM-multiplexing prototype. Experimental results indicate successful real-time transmission of 139.2 and 104.0 Gb/s at distances of 22.5 and 45 m, respectively. We also demonstrated real-time OAM multiplexing transmission in a reflection scenario using a reflector placed in the middle of the propagation path and successfully achieved 139.2 Gb/s at a distance of 22.5 m.

The use of millimeter wave (mmWave) bands is a promising approach to increase the capacity of mobile networks. However, the mm Wave link quality (LQ) is strongly affected by the condition of the line of sight (LOS) path. In order to stably utilize mm Wave bands, an effective solution is to predict future LQ and adaptively control wireless communication. LQ prediction based on Deep Neural Network (DNN) using physical space information, such as the position information of user equipment (UE), is effective to enable the proactive control. On the other hand, huge training data is required to provide the prediction model. In a supervised learning approach, the prediction models must be generated for all the base stations with which the UE communicates. Thus, it is a key to generate the prediction models for different base stations using no or less amount of the training data. In this paper, we propose a method for preprocessing physical space information to reduce the amount of training data when the UE accesses to the different base station. The proposal normalizes the position and direction information of UE by using the position of the base station. To evaluate the effectiveness of the proposed method, we conducted experiments using an automated humanoid robot in a commercial 5G network. Experiment results show that the proposed method reduces the root-mean-square-error (RMSE) of the differences between the predicted and actual throughputs by 35.1 and 51.0% at the different base station when using no samples and 100 samples as training data, respectively, compared to without physical space information preprocessing.

The terahertz (THz) band above 100 GHz is a promising resource for high capacity wireless transmission in future wireless networks due to the availability of ultra-wide bandwidth over 10 GHz. In order to realize ultra-high-capacity wireless transmission by exhaustively utilizing the resource, we are focusing on orbital angular momentum (OAM) multiplexing transmission technology using uniform circular array (UCA) in the sub-THz band. The OAM have a larger beam divergence than plane waves, resulting in shorter transmission distance range, thus, it is effective to widen the beamwidth and reduce the divergence by using reflector. However, it is necessary to consider shielding and scattering by the array antenna itself and other structures and precisely maintain the orthogonality between OAM modes in practice. In this paper, we propose off-axis double-reflector antenna designs for rotationally symmetric array antenna such as UCAs to extend the transmission distance by enlarging their effective array size while reducing the self-shielding and scattering problem. Experimental end-to-end transmission evaluation shows the proposed reflector can properly maintain the orthogonality between OAM modes, and our off-axis double-reflector design is feasible for Tbps-class long distance OAM-MIMO multiplexing transmission.

Millimeter-wave (mm-wave) band use is the key to meet the demand for high-speed wireless communication in future mobile networks. The mm-wave throughput performance is often unstable due to the influence of blocking objects, the fading effect, etc. A promising way to improve the throughput performance of the mm-wave channel is for multiple user equipment (UEs) to be used by the user and to integrate their links to achieve the extremely high requirements of the future mobile network. Since the mm-wave channel throughput strongly depends on where the UE is kept, the simple cooperation of multiple-UE-link of the same user enables stable throughput performance, while there is little gain in the multiple-UE-link cooperation in the micro-wave channel. In this paper, we evaluated the potential throughput of multiple-UE-link cooperation by using measured throughputs in 28 GHz channels in a fifth generation mobile communication system (5G) network. In addition, we propose the link selection by using measured reference signal received power (RSRP) cross-correlations between UE links to reduce power consumption. Experimental results show that the potential throughput of three UE links is 22.2 % greater than when using a single UE link at the 5%-ile cumulative distribution function (CDF). Also, we show that the proposed method achieves throughput performance with two UE links that is the same as when three UE links are used at the 5%-ile CDF and is 9.9% greater than that of the conventional link selection.

Recent advances in wireless communication technology such as fifth-generation (5G) have enabled the creation of various novel applications. As a result, a large number of devices are now being connected to mobile networks, and mobile traffic is increasing year by year. Although the use of the millimeter-wave (mmWave) bands is a promising approach to increasing the capacity of mobile networks, there are many challenges to use mmWave bands. The link quality (LQ) of mmWave wireless links is impacted by the surrounding objects. Therefore, in order to stably utilize mmWave bands, we believe that it is necessary to predict future LQ predictions and adaptively control wireless communications. In this paper, we evaluated the throughput prediction methods using physical space information of the target UE and surroundings in a commercial 5G network. The evaluation entails measuring the throughput in an actual indoor environment where both the target UE and surrounding objects are moving. To create the huge dataset necessary to allow the moving terminal holder and surrounding pedestrian (objects) to be modelled, we develop two autonomous humanoid robots and make one move so as to block the LOS of the other robot, which is the UE holder. The experiments shows that our proposed method using physical space information yields a 57.5 % improvement in prediction accuracy at the 50th percentile absolute error value over a naive prediction model that uses past throughput information.

Mobile traffic growth requires the advancement of not only the wireless access networks but also their backhaul and fronthaul. Terabit-class wireless backhaul and fronthaul can be an alternative to optical fiber transmission and will be one of the key technologies to construct a more flexible and less expensive network infrastructure for sixth-generation mobile networks (6G). However, it is a challenge to provide an extremely high-capacity wireless link for point-to-point connection without spatial multiplexing gain obtained by the multi-path rich environment. We demonstrated the world’s highest wireless transmission data rate of 1.58 Tbps in the sub-terahertz (sub-THz) band for 6G backhaul and fronthaul networks on the basis of the orbital angular momentum (OAM) multiplexing technology with a wideband Butler matrix. Terabit-class wireless transmission was achieved by designing a wideband 8×8 Butler matrix with two types of 3-dB couplers for the structure without crossover and differential phase shifters that give the desired phase difference over wide bandwidth. Our Butler matrix is capable of multiplexing eight OAM beams and show a high mode isolation of greater than 15 dB and low insertion loss of less than 1.5 dB from 135 to 170 GHz. We implemented the Butler matrices in our OAM multiplexing transmission system, in which the physical-layer data rate of 1.58 Tbps wireless transmission was confirmed with eight OAM modes and dual polarization using the 32 GHz bandwidth.

The evolution of a cell phone network and a wireless local area network (LAN) has enabled various devices to be connected to wireless networks anytime, anywhere. This has led to the emergence of various applications, such as automatic transportation of parts and cargo by automated guided vehicles, remote control of unmanned vehicles, and drones. It will be a challenge to maintain high-quality connections, which satisfy the requirements of such emerging applications, for a large number of intelligent connected devices. To improve the network performance, we must control the wireless network setting and the behavior of the connected vehicles, i.e., robots. From this perspective, this proposes a novel framework, CoRein, that achieves simultaneous optimization of robot behavior and wireless network setting. In contrast to the existing studies, CoRein provides optimal actions for robots and wireless networks using deep reinforcement learning (DRL) architecture. To the best of our knowledge, our study firstly attempts to solve different tasks corresponding to robots and networking with a unique DRL architecture. To evaluate the effectiveness of CoRein, we considered a scenario where two robots were moving back and forth while communicating with one of two access points. CoRein calculates the robot velocities and AP selection. We carried out simulations and indoor experiments with our robot testbeds to evaluate the network performances. Evaluation results confirmed that the two robots adjusted their positions and the access points to increase the wireless network's performance, i.e., throughput and round trip time while changing the speed of their movements.

This paper proposes a feature extraction method from three-dimensional (3D) point cloud employing spatial frequency analysis for point cloud-based link quality prediction. The proposed method aims to address the human blockage problem in millimeter-wave (mmWave) communications, where proactive communication control through machine learning-based future link quality prediction has been shown to be effective in preventing link quality degradation. However, the use of 3D point clouds presents challenges such as increased transmission cost and computational complexity due to the large data size. To address these challenges, we propose a preprocessing method that can efficiently extract features from the point cloud and reduce data size without compromising the accuracy of mmWave link quality prediction. Our approach uses spatial frequency-based filtering to isolate signals related to moving obstacles, such as pedestrians, in the frequency domain. It also removes large, static background objects like walls and furniture. The proposed method can extract relevant objects in the point cloud based on their spatial frequency without requiring object detection or segmentation. The experimental results demonstrate that our proposed method significantly reduces feature data size by approximately 99% compared to conventional methods, while still maintaining high link quality prediction accuracy.

Given the recent advances in wireless communication technology, all things are being connected to networks, and it is expected that various new and novel applications will be created. The diversification in applications will yield various requirements for wireless communication. We believe that autonomous robot services will be popular after COVID, and the management, monitoring, and operation of the mobility robots requires highly reliable wireless links. In this paper, we propose a wireless link quality prediction system that uses camera images. The proposal generates a prediction model for each camera and combines the output of the models using weights calculated by the outputs of reliability models. By providing separate prediction models for each camera, the prediction system easily handles new additional cameras and drops the cameras found to have low reliability. Indoor experiments show that the proposed prediction scheme outperforms the prediction method that uses all camera images as input features without regard to their reliability.

This paper demonstrates the feasibility of predicting the future received power of millimeter-wave (mmWave) communication using point cloud data. To mitigate the human blockage problem in mmWave communication, previous works have studied a camera-vision assisted mmWave link quality prediction, which predicts the time-series of the received power from the next moment to as many as several hundred milliseconds ahead by leveraging camera imagery and machine learning. However, camera imagery generally includes privacy-sensitive information, which induces privacy concerns in the camera-assisted mmWave networks. In this paper, we demonstrate that point cloud, which can be obtained by light detection and ranging (LiDAR) sensors and poses fewer privacy concerns than camera imagery, can be an alternative to the cameras in mmWave link quality prediction. Specifically, we propose a mmWave received power prediction method using point cloud data, and our experimental evaluation demonstrates that the proposed method predicts mmWave received power 500ms ahead with a root-mean-squared error of 3.3dB, which is comparable to the existing camera-based method. Moreover, we verify that even minor environmental changes can degrade the accuracy of the trained prediction model and this degradation can be mitigated by model fine-tuning with a small dataset.

The link quality prediction facilitates high quality video streaming over mobile networks. However, the existing link quality prediction algorithms focus on minimizing the gap between the ground truth and the prediction result, while it remains a challenge to exploit such information to achieve high quality video streaming with minimum Quality of Experience (QoE) degradation. The accurate link quality prediction is one of keys to enable beyond 5G/6G world. In this paper, we produce artificial intelligence (AI) based link quality prediction which consists two steps: 1. We explore and exploit the temporal correlation in time series to adaptively learn and predict its short-term behavior based on Gaussian Process (GP). 2. The GP-based prediction is tailored to maximize QoE by finding a proper piece-wise convex envelope of the predicted link quality in an online manner. By using the measured uplink throughputs, the video streaming QoE of the proposed framework were evaluated.

With accurate knowledge of future Channel State Information (CSI), it becomes possible to better comprehend the radio propagating environment and manipulate the wireless resources in a proactive manner, so as to provide solid support to smart and high quality wireless transmission. However, in mobile environment, the evolving correlation patterns in CSI series challenge the existing data-driven algorithms to adaptively learn and predict its behavior. In this article, an adaptive learning algorithm is proposed based on Gaussian Process (GP), to discover and utilize the spatial correlation within a channel and across channels, and produce accurate CSI prediction. Specifically, 1). To track the evolving correlation of a channel, we tailor Spectrum Mixture (SM) kernel to not only approximate the optimal kernel adapting to the current CSI, but also capture the combined effect of path loss and User Equipment (UE) motion. 2). The correlation across channels is encoded into the GP-based learning framework through Linear Model of Co-regionalization (LMC). Finally, we verify the performance improvements through simulation.

Vehicle-to-everything (V2X) communications is one of key to further developing intelligent transport systems. In 2017, the long-term evolution V2X (LTE V2X) was specified as Release 14 of the 3rd generation partnership project (3GPP) and has attracted significant attention. In addition to uplink/downlink communications, LTE V2X supports direct communications that are referred to as sidelink (SL). Since SL communications are expected to be used to actualize road safety use cases, its reliability must be investigated. This paper proposes a SL transmission scheme that reduces the rate of packet collisions due to concurrent resource reselection by other sets of user equipment (UEs). In the proposed scheme, a UE that is going to conduct resource reselection in the next transmission performs pre-resource selection, and then notifies other UEs of the resources virtually reserved by that UE. Effectiveness of the proposed scheme is verified by computer simulation.

This paper reported field experimental results of sensor data transmission for vehicle-infrastructure cooperation by using the 5G network. High-definition data of many sensors are collected from vehicles and infrastructures by 5G network. An integrated analysis system distributes traffic information to vehicles by using the collected data, and vehicles can drive safely and reliably regardless of road environment such as the situation around blind corners. In order to evaluate the integrated analysis system, we established the 5G experimental system in the test course. The 5G network collects raw sensor data of the LiDAR, camera, and millimeter wave radar equipped on the vehicle and infrastructure, and distributes real time traffic information. Moreover, the system latencies, which are composed of Data Collection Time, Analysis Time, and Distribution Time, were evaluated. We classify traffic information in 4 categories (location, detail attribute, behavior, and prediction of behavior), and evaluate whether system latency of the integrated analysis system is reasonable in terms of driving support. In the field experiments, the 5G based end-to-end system latencies were clarified by using driving vehicle and road infrastructures equipped with the 5G testbed in the test course including several intersections.

Interworking among heterogeneous wireless networks across licensed and unlicensed spectra has gained much attention to support the surge in mobile traffic. Wireless LAN (WLAN) is well known as the dominant wireless network application in the unlicensed spectrum. Unlicensed spectrum access should follow carrier sense multiple access/collision avoidance (CSMA/CA) to share the wireless resource with existing WLAN nodes. However, the transmission throughput with CSMA/CA can be significantly degraded by the hidden terminal problem in environments where huge mobile traffic depletes the wireless resources. In this paper, we propose a channel access acquisition mechanism that uses licensed spectrum access. The proposed mechanism significantly reduces the impact of the hidden terminal problem by using both transmission and reception opportunities. So that the receiver can efficiently get reception opportunities, the information on the user data waiting at the transmitter is notified by using a licensed spectrum channel. Computer simulations show that the proposed access mechanism enables robust transmission even if the wireless resource at the receiver is depleted by transmission from nodes hidden from the transmitter.

Multiple-input multiple-output (MIMO) systems with implicit feedback utilize uplink channel state information (CSI) for downlink transmit beamforming, relying on over-the-air channel reciprocity. We develop real-time hardware for multiuser MIMO transmission with implicit feedback based on orthogonal frequency division multiplexing (OFDM), with field programmable gate arrays (FPGAs). This paper presents indoor experimental results by the real-time hardware, employing several calibration schemes for uplink CSI. In particular, we investigate performance of our proposed scheme, weighted-combining calibration (WCC), in which multiple calibration coefficients are calculated and then combined with weights based on channel gain, to improve the calibration accuracy. It is shown that WCC improves real-time throughput considerably, compared with equal gain-combining calibration (EGCC), a scheme which combines coefficients with equal weighting.

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.

This paper proposes a novel calibration scheme for implicit feedback beamforming on downlink (DL) multiuser (MU) multiple-input multiple-output (MIMO) systems. In the proposed scheme, an access point (AP) calculates calibration coefficients from ratios of DL and uplink (UL) channel state information (CSI) corresponding to multiple stations (STAs), and then combines multiple coefficients with minimum mean square error (MMSE) weights. Although it is difficult to calculate MMSE weights since Gaussian noise terms due to estimated CSI error exist in the denominator of the mean square error, we derive the weights using a linear approximation in the highsignal to noise power ratio (SNR) regime. Simulation results reveal that the proposed scheme obtains signal to interference plus noise power ratio (SINR) gain of more than 6.9 dB over a calibration scheme without combining, at 10% cumulative distribution function (CDF) and SNR of 40 dB. It is found that the proposed scheme improves the calibration accuracy considerably.

MIMO-OFDM with a massive number of transmit antennas (Massive MIMO-OFDM) promises to increase the spectrum efficiency or reduce the transmission energy per bit. The performance of Massive MIMO-OFDM is strongly influenced by the method used to estimate the channel state information (CSI) at the transmitter. Given the massive number of transmit antennas, the many training frames needed for CSI estimation decreases MAC efficiency and increases the cost of estimating CSI at a user station (STA). This paper presents a CSI estimation scheme that reduces the training frame length by using a rank enhancement pilot design. This design assigns a different CSI estimation weight to each subcarrier. The STAs feed unitary matrices, that are obtained by multiplying the left and right singular vectors, back to the AP. The proposed CSI method enables the AP to obtain accurate CSI for limited signal spaces and less-accurate CSI for wider signal spaces for setting data transmission weights and CSI estimation weights, respectively. Simulations of an IEEE802.11n channel show that the proposed CSI estimation scheme with very short training frames obtains greater than 95% of the achievable bit rate of a full CSI estimation in Massive MIMO-OFDM systems with 32 transmit antennas, 2 receive antennas, and 3 STAs.

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.

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.

Multiuser (MU) - multiple input multiple output (MIMO) techniques are attractive to increase the downlink spectrum efficiency since they can accept a small number of antennas at each user station (STA). In MU-MIMO systems, the transmission performance is affected by the STA combination that is spatially multiplexed in the same frequency and same time slot. Thus, the AP needs to determine the optimum STA subset and the number of spatially multiplexed STAs from the channel state information. In this paper, we propose a user selection scheme that uses an iterative algorithm to determine the number and the optimal combination of multiplexed STAs. The proposed scheme chooses STAs one by one so as to improve the throughput of all STAs in the subset compared to that achieved in single-user MIMO transmission. Thus, all STAs have a fair chance of being selected even when their SNRs vary widely. Furthermore, the iterative algorithm has lower calculation complexity than the exhaustive search approach. The index employed for user selection is also simplified by using the signal-space vectors of the channel matrices. Computer simulations confirm that the proposed method improves the increase in STA throughput over single-user MIMO throughput in a wide-range of SNR values while equalizing STA selection probabilities.

Frequency-dependent IQ imbalance will be one of the degrading factors for future wideband wireless communication systems. This paper presents a blind compensation scheme for the frequency-selective IQ imbalance that occurs in wideband OFDM transceivers. The proposed scheme uses 2-tap filters having weights calculated by a constant modulus algorithm, which does not require any training or pilot signals. We verified by computer simulation that our proposed scheme compensates for frequency-dependent IQ imbalance in OFDM transceivers. We also show some experimental results, in which a frequency-dependent IQ imbalance over 6 GHz is compensated for by our proposed scheme.

Multiple-Input Multiple-Output (MIMO) system has been actively investigated to enhance wireless data transmission in outdoor environments. Micro/Macro-cell services such as WLAN/WiMAX have been much attention because they achieve a high throughput. Since proliferation of these services are expected, base stations (BSs) need to be located at various heights and locations in outdoor environments. Although many publications have reported measurements of the channel state information (CSI) in outdoor environments, the relationship between the BS heights or locations and the transmission performance in MIMO systems has not yet been fully investigated. Moreover, when the number of BSs increases per area, inter-cell interference problem occurs. Such an interference deteriorates the communication quality. In this paper, the experiment is conducted in urban area, in Japan, and the CSI was obtained using 4 × 4 MIMO-OFDM signals. We present signal to noise ratio (SNR), channel capacity and eigenvalues in MIMO channels, when the BS heights and locations are changed. We clarify that eigenvalues are one of important parameters for not only the channel capacity in MIMO channels but also inter-cell interference by the analysis using the measured MIMO channels.

Recently, the multiuser multiple input multiple output (MU-MIMO) system has attracted much attention as a technology that enhances the total system capacity by generating a virtual MIMO channel between a base station and multiple terminal stations. Although extensive evaluations are needed because the number of system parameters in MU-MIMO is much larger compared to the single user (SU)-MIMO, the performance of MU-MIMO in actual environments is still not well understood. This paper describes the features and effectiveness of a 16times16 multi-user (MU)- MIMO testbed in an actual indoor environment. Moreover, we propose a simple adaptive modulation scheme for MU-MIMO-OFDM transmission that employs a bit interleaver for the frequency and space domains. Finally, we evaluate the frequency efficiency by obtaining the bit error rate of this testbed in an actual indoor environment. We clarify that 16times4times4-user MU-MIMO transmission using the proposed modulation scheme achieves the frequency utilization of 870 Mbps and 1 Gbps (SNR: 31 and 36 dB) with 20 MHz bandwidth, respectively.

Multiuser (MU) - multiple input multiple output (MIMO) techniques were proposed to increase the spectrum efficiency because only a few antenna branches are allowed at a simple mobile terminal (MT). Among the MU-MIMO techniques, the block diagonalization (BD) algorithm is well known as a practical linear precoding technique for a downlink. In this paper, we analyze an achievable bit rate in MU-MIMO using BD algorithm and derive the simplified expression using the signal spaces of users. Based on the derived expression, two kinds of user selection methods for the BD algorithm (USBD) in MU-MIMO systems are proposed. The proposed methods estimate the improvement in the achievable bit rate compared to that in a single user (SU)-MIMO system using the signal spaces of the supported users. The first proposed method is USBD with a full search (USBD-FS) and it selects the combination of users to achieve the highest total throughput by considering all the combinations. The second proposed method is USBD with a tree structure search (USBD-TS), which drastically reduces the calculation complexity. Although all combinations of users are not considered in USBD-TS, a sufficient achievable bit rate is obtained while the calculation load is much less than that for the conventional method. Computer simulations confirm that USBD- FS and USBD-TS obtain the achievable bit rate of 100.0% and 91.1% compared to that for the best combination of users when the angle spread at the transmitter is 75deg.

The multiple input multiple output (MIMO) technique enables the application of spatial multiplexing as a way of increasing the spectrum efficiency. However, only a few antenna branches are allowed at a simple mobile terminal (MT) and the MIMO effect is limited for those MTs. Multiuser (MU)-MIMO techniques were proposed to support such simple terminals to overcome such problems. Among them, the block diagonalization algorithm is well known as a practical linear precoding technique for downlink in the MU-MIMO systems. Because the number of users supported by the access point is limited and the improvement in the spectrum efficiency greatly depends on the combination of spatially multiplexed user, a simple transmission performance estimation method is needed. In this paper, we propose a new capacity estimation method for MU-MIMO systems. The proposed method estimates the achievable bit rate in MU-MIMO while significantly reducing the calculation load compared to that for direct estimation in the block diagonalization algorithm. Theoretical analysis and the simulation results confirm the validity of the proposed method.

Multiple input multiple output (MIMO) systems have attracted much attention as they can provide increased channel capacity within a limited frequency band. In some important applications, such as wireless LAN and cellular systems, MIMO systems will likely operate in environments for which a single base station should simultaneously communicate with many users. Such applications require development of multiuser(MU)-MIMO techniques. The prior work on MU-MIMO systems have resulted in derivation of the ideal channel capacity as well as various proposed schemes for transmission control and scheduling. In order to incorporate MU-MIMO technologies into commercial systems, extensive evaluations are needed, since the amount of system parameters in MU-MIMO is much larger as compared to the Signleuser(SU)-MIMO. Due to such a volume of system parameters, the performance of MU-MIMO in actual systems is still not well understood. This paper describes MU-MIMO testbed which can deal with 8times8 MIMO-OFDM transmission based on the IEEE 802.11a signal format. We have conducted experiments in an actual indoor environment in order to evaluate the frequency utilization for downlink scheme implemented in the MU-MIMO testbed. Finally, we discuss the effectiveness of MU-MIMO for SU-MIMO when the number of receiving antennas is changed

Multiple input multiple output (MIMO) systems increase the channel capacity within the limited frequency band. MIMO techniques can be combined with orthogonal frequency division multiplexing (OFDM), which is known to combat frequency selectivity in wireless channels. When channel state information (CSI) is available at the transmitter, the eigenvector beamforming method is well known to achieve the maximum channel capacity. However, the performance of eigenvector beamforming, which employs both modulation and forward error correction schemes, is not very well understood. In this paper, we evaluate the channel capacity and the frequency utilization of an eigenvector beamforming method in an actual indoor environment using an 8times4 MIMO-OFDM testbed that we developed. The experiment results indicate that the transmission bit rate increases offered by the eigenvector beamforming method in 4times4 MIMO and 8times4 MIMO channels are 3.83 fold and 4.90 fold larger than the performance of conventional transmission in SISO channel respectively and these ratios are larger than the increases in the channel capacity

This work proposes a novel MIMO-SDMA configuration using dual polarization antennas at the base and terminal stations. In this scheme, based on the transmission speed required by the terminal stations and the spatial correlation between the terminal stations, the base station determines the combination and number of antennas on which the terminal stations transmit Thus, the proposed configuration obtains high spectrum efficiency while improving the transmission speed required for broadband wireless communications. Moreover, the proposed configuration improves the output SINR compared to the conventional SDMA when multi-rate transmission is employed based on computer simulation.