Applying reconfigurable intelligent surfaces (RIS) to control propagation channels effectively can lead to more efficient 5G networks in terms of both spectrum and energy utilizations. However, the increase in the number of RISs leads to a corresponding rise in power consumption. This paper proposes a novel control method for distributed-RISs by considering a system configuration in which multiple RISs are deployed within the coverage area of a single 5G mmWave BS under dynamic blockage scenarios. The proposed method adaptively selects deployed distributed RISs based on the radio environment to enhance wireless communication quality while simultaneously reducing the power consumption of RISs, thereby contributing to the realization of green 5G mmWave networks. Through ray-trace simulations conducted in the urban environment of Shibuya, Tokyo, we evaluate the effectiveness of the proposed method. The results confirm that it improves received power while achieving approximately a 60% reduction in power consumption.

In blockchain-based wireless access sharing, the user equipment (UE) and base station (BS) make a connection contract by using blockchain technology. This enables UEs to temporarily utilize various BSs securely, including private BSs, without a centralized control station. However, the feasibility of such a system with connection establishment and contract authentication flows consistent with wireless standards has remained unclear. This paper describes an actual system for sharing blockchain-based wireless access among individuals with wireless LANs and shows its feasibility. It also evaluates our proposed accommodation control method in a real-world environment. It is found that this method can improve the median downlink throughput by a factor of 1.8 compared with not using it.

In blockchain-based radio access network sharing (B-RAN), user equipment (UE) and a base station (BS) make a connection contract using blockchain. This enables UEs to utilize various BSs securely, including private BSs, without a centralized control station. However, since the selection of BSs and connection contracts between UEs and BSs are done individually, UE connections may be concentrated at specific BSs even when other BSs are available. This may occur degradation of communication quality. We therefore propose a method of controlling connection parameters by extracting UE transition information and the number of accommodated UEs from the blockchain ledger. Our method can smooth the number of accommodated UEs with autonomous and decentralized offer-price control. The results of computer simulations demonstrated that the proposed method could smooth the communication quality provided to UEs regardless of biased UE placement.

In this paper, a system for predicting degradation of wireless-communication quality in the case of autonomous vehicles is proposed. It uses historical-data-based throughput estimation using a neural network trained on location information and past performance. A method for improving the accuracy by fine tuning is also proposed. We evaluate the accuracy of throughput estimation using the proposed system in two outdoor use cases (field and urban) and one indoor use case. The results show that the proposed method of fine tuning using a large amount of received power data and a small amount of throughput data can improve the estimation accuracy of throughput.

Realizing extreme reliability for Internet of Things (IoT) communications is one of the major milestones paving the way towards Beyond 5G (B5G) and 6G. In this work, we investigate the issue of improving the reliability of packet transmissions in the absence of prior knowledge of network statistics, nor of instantaneous Channel State Information (CSI), for B5G Sub-6GHz/mmWave integrated networks. Specifically, the aim is to maximize the global successful packet reception at devices, while guaranteeing their individual Packet Loss Rate (PLR) requirements. The proposed method exploits a newly developed approach of Risk-Averse Reinforcement Learning (RARL), for exploiting multi-connectivity over Sub-6Hz and mmWave interfaces. Namely, the Access Point (AP) is able to optimize its interface selection decisions despite the unknown dynamics of the wireless environment based on limited feedback from its associated devices, so as to increase reliability under low delay and resource consumption. Numerical results show that, the proposed method significantly improves the global reliability performance by rapidly learning and adapting its decisions as compared to baseline methods.

In this work, we investigate the problem of joint user-to-access points (AP) association and beamforming in an integrated sub-6GHz/mmWave system. The goal is to maximize the long-term throughput of the system, while satisfying a large number of heterogeneous user QoS requirements in a distributed manner. We propose a method based on Deep Q-Networks (DQN), where each user self-optimizes its AP association and interface requests, and can be served by several APs simultaneously for supporting multiple applications. Based on these requests, each AP selects its associated users and applications served on each interface, while optimizing its mm Wave beamforming parameters. Simulation results show that, compared to baseline DQN schemes among which the Action Elimination (AE)-DQN, the proposed method enables to fine-tune the selection of APs and interfaces to the specific level of each required QoS, thereby achieving a high global throughput while notably reducing user outage probabilities1.1.This collaborative research project is funded by NTT Corporation, Japan.

This work investigates the design of a sustainable Deep Q-Network (DQN) implemented at the user device, whose purpose is to optimize the user’s association to multiple access points (AP) in a Beyond 5G (B5G) Sub-6GHz and mmWave integrated network. To better cope with dynamic mobile environments, we first propose an adaptive $\varepsilon$-greedy policy at each user’s DQN in order to maximize the long-term sum-rate while simultaneously satisfying the Quality of Service (QoS) constraints of different applications. We then provide the detailed analysis of the energy consumed by each user device, in particular the power for DQN processing and for data movement. The trade-off between network performance in terms of sum-rate and QoS outage probability, and energy consumption at the user side is evaluated. Numerical results not only show the effectiveness of the proposed method compared to baseline, but also reveal the tremendous energy costs required by the default user DQN, underscoring the paramount importance of the proposed trade-off aware user DQN design1.

Future wireless networks will be facing unprecedented difficulties arising from mobile traffic growth, network densification, as well as diversification of applications and services. Indeed, future user devices are expected to integrate diverse radio interfaces such as 5G, WBAN or IoT, enabling each user to be served a wide range of applications at any time. This poses significant challenges in terms of wireless resource sharing and interference management, as more and more stringent Quality of Service (QoS) constraints should be jointly satisfied in dense interfering environments. Furthermore, future networks are expected to be highly autonomous and decentralized. To meet these challenges, this work proposes distributed user-to-multiple Access Points (AP) association methods, where the objective is to maximize the long-term sum-rate subject to application QoS constraints, as well as to AP load constraints. Our distributed methods enable each user to leverage their Deep Reinforcement Learning (DRL) capabilities, in particular Deep Q-Learning (DQL), to self-optimize their APs’ selection solely based on their local network state knowledge, so as to best satisfy their diverse requirements. Numerical results show that, compared to baseline schemes, the proposed methods enable global throughput enhancements while reducing user QoS outage probabilities, even in large and dense networks.

In future wireless networks, more and more users will be requiring various applications provided by multiple wireless interfaces simultaneously. This poses significant challenges for enabling efficient wireless resource sharing while satisfying the diverse and stringent Quality of Service (QoS) constraints, especially in dense interfering networks. In such a context, this work proposes distributed user-to-multiple Access Points (AP) association methods, where a user requiring several applications may be served by several APs simultaneously. The problem is formulated as a network sum-rate maximization subject to the required QoS constraints for each user and application, and AP load constraints. In the proposed distributed association methods, each user can decide to associate to multiple APs simultaneously using its locally available network information, leveraging reinforcement learning techniques. Simulation results show that, compared to a baseline scheme, the proposed methods enable large throughput enhancements while satisfying the QoS constraints and AP load limitations, thereby reducing user outage probabilities.

In this paper, we make a summary report of our 5G R&D activities for “High Capacity Technologies with Ultra High-Density Multi-Band and Multi-Access Layered Cells”, funded by the Ministry of Internal Affairs and Communications (MIC) in Japan. This national project consists of three research subjects: “Ultra high-density distributed antenna systems”, “Optical access technologies accommodating highly densified small cells” and “Multi-RAT system technologies for multi-band and multi-access layered cells”. We describe the research and development result for each subject.

This paper describes a novel wireless resource allocation method we have developed with distributed architecture and the DB search algorithm. Computer simulation results revealed that the method improves throughput 1.2 times on average compared to the conventional fixed allocation method with centralized architecture. It also reduces the computational complexity by three digits compared to the conventional metaheuristic method. Our method will make it possible to achieve large scalability of wireless resource optimization on future wireless access systems, such as multi-RAT, multi-cell environments of 5G and 6G, or high density wireless LAN systems.

This paper describes cooperative wireless LAN (WLAN) control methods we propose for high density environments, with the focus on IEEE 802.11ax WLAN systems. It also shows how they were evaluated by computer simulation. Two control schemes are proposed, the first of which optimizes spatial reuse parameters and access network selection. This scheme improves user equipment throughput 1.7 - 2.4 times in terms of throughput distribution (median value). The second scheme is for cooperatively scheduling trigger frames with hierarchical clustering. This scheme enables capacity to be improved by up to 11.3% when WLAN access points are deployed closely. These control schemes are potentially effective for IEEE 802.11ax WLAN systems utilized as 5G mobile network components.

Interference alignment (IA) is a technique that can suppress interference with a small number of antennas by aligning interference signals using transmit weights. These weights are designed based on the channel state information (CSI) fed back from each receiver, however, under the timevarying channel, the estimated CSI can be delayed/outdated, which will result in an imperfect IA. Therefore, IA with channel prediction has attracted much attention. The auto regressive (AR) model is known as a prediction method that predicts a future state based on only the past states. In the conventional channel prediction based IA method, the past channels are used directly for prediction. Therefore, the number of calculations for prediction can be too large. In this paper, based on the AR model, we describe a low complexity and high accuracy channel prediction method for IA. To predict the future channel, we only use the differences of channels between adjacent times instead of using the past channels directly. This will lead to a very low channel prediction error. Simulations show that the proposed method improves prediction accuracy and requires less calculation than the conventional one. Moreover, the IA with the proposed channel prediction method will achieve a higher transmission rate.

Throughput starvation, which occurs due to the well-known exposed/hidden terminal problem in CSMA/CA based channel access mechanisms, will be a critical problem for dense wireless LAN systems. To address this problem, we previously proposed schemes that identify and manage starved devices by monitoring the beacon signals transmitted by access points (APs) and showed that they can identify low throughput devices and have potential to improve WLAN system throughput. This paper describes experimental results obtained for schemes that identify starved APs and manage their channels. Numerous experiments verified that the schemes can identify APs that have quite low throughput as starved APs and that management can reduce the low throughput APs in real environments. We also indicate that it is necessary to consider beacon transmission delay fluctuation when determining the threshold used for identifying starved APs in cases such as the surrounding environment fluctuating in a short period.

Massive multiple-input multiple-output (MIMO) is a promising technology for the next generation of wireless communication systems, which improves the spectrum efficiency and reduces the energy consumption. However, because of the shortage of both time and frequency resources, it is inevitable to reuse pilot sequences among users, which leads to a degradation in terms of channel state information (CSI). This degradation is known as pilot contamination. For the purpose of solving this issue, the pilot reuse (PR) scheme was proposed to alleviate the pilot contamination by non-overlapping the angles of arrivals (AOAs) between the signals of the identical pilot sequences. In the conventional AOA-based PR schemes, there is a difference in channel estimation accuracy between users, according to the order of pilot assignment, because these PR schemes assign greedily pilot sequences to user, which provides the most benefit for the first user, and less benefit for later users. In this paper, we propose a location-based PR scheme to alleviate the pilot contamination by assigning pilot sequences based on the path-loss as well as the AOAs. Furthermore, a low-complexity coordinated pilot assignment algorithm that brings benefits across multiple cells is introduced. Simulation results show that the proposed scheme provides a good channel estimation accuracy and fairness among users.

This paper presents path loss frequency dependence on the basis of measurement campaign results in two urban macro (UMa) cell environments using four frequencies between 2 and 66 GHz. For a 3rd Generation Partnership Project (3GPP) UMa model above 6 GHz band, the dependence is shown by 20log(f). However, some studies for the environments show the frequency dependence more than 20log(f) and its propagation phenomena as its cause. The 3GPP model is based on measurements whose dynamic range and distance are limited. So, we measured the path loss characteristics using wide dynamic range equipment capable, for example, of measuring the path loss more than 170 dB at 26 GHz. The measurement results show that the dependence is similar to 3GPP model in Line-of-Sight areas, but that in Non-Line-of-Sight (NLoS) areas it increases more than the dependence of 20log(f). The measured path loss also show that the high frequency dependence can be seen in far distance area and the high path loss area. Taking into account the geographical and urban environment, the high frequency dependence in the far distance NLoS area may be explained by the dominance of diffraction over reflective propagation along the buildings. These results can contribute to clarify the site-specific path loss characteristics and design the cell area for the high frequency bands.

We present centralized iterative algorithms that jointly determine the optimal transmit and receive filters as well as the optimal power allocation for a K-user multiple-input multiple-output (MIMO) interference channel (IC). The optimality criterion is based on the achievable sum-rate and the average per user multiplexing gain in the MIMO IC. By allowing channel state information (CSI) exchanged between base stations (BSs) and a central unit (CU), we design a feedback topology where CU collects local CSIs from all BSs, computes all transmit and receive filters and sends them to corresponding user-BS pairs. Note that the local CSIs at BSs are obtained from the estimation of the channel states during the so- called uplink-training phase. At the CU, we propose iterative algorithms utilizing alternating optimization strategy to design the filters. In most of the studies on the MIMO IC, choice of equal transmit powers for all user-BS pairs ignores the essential need to search for the optimal power allocation policy; they do not take the full advantage of the system's total power. Thus, how to allocate power among all the user-BS pairs in the network based on the sum-rate maximization strategy and under a sum power constraint is another key to this paper.

We present centralized iterative algorithms that jointly determine the optimal transmit and receive filters as well as the optimal power allocation for a K-user multiple-input multiple-output (MIMO) interference channel (IC). The optimality criterion is based on the achievable sum-rate and the average per user multiplexing gain in the MIMO IC. By allowing channel state information (CSI) exchanged between users and a central unit (CU), we design a feedback topology where the CU collects local CSIs from all base stations (BSs), computes all transmit and receive filters and sends them to corresponding user-BS pairs. At the CU, we propose iterative algorithms utilizing alternating optimization strategy to design the filters. In most of the studies on the MIMO IC, choice of equal transmit powers for all user-BS pairs ignores the essential need to search for the optimal power allocation policy; they do not take the full advantage of the system's total power. How to allocate power among all the user-BS pairs in the network based on the sum-rate maximization strategy is the key to this paper. Thus, while the filters are designed at the CU, we propose a novel power allocation problem for sum-rate maximization under sum power constraint.

Massive MIMO (multiple-input multiple-output) is one of the key technologies to realize 5G (5th Generation). Massive MIMO can be implemented with many antennas at a transmitter and receiver sides, and it can improve transmission quality at high frequency band by transmitting with superposing shift of radio wave toward the direction of the receiver. However, there exists an issue such as the increase of the amount of feedback of channel state information (CSI) from the receiver to the transmitter, due to the enormous number of antennas. For the purpose of solving this issue, there exists the technique to compress CSI to a lower dimension matrix and decrease the amount of feedback, by using principal component analysis (PCA). In the conventional method, the compression matrix to compress a channel matrix is calculated on the basis of PCA, and the compressed channel is fed back from the receiver to the base station (BS). In this method, the compression matrix used in PCA is generated based on the past CSI at the receiver, which leads to the degradation of transmission rate. This is because there is a mismatch between the CSI acquired at the transmitter and that when the transmitter transmits a signal, due to the channel variation during the feedback from the receiver to the transmitter. In this paper, to solve this problem, we propose the method based on PCA with the channel prediction. As the channel prediction, the forward-backward AR (Auto Regressive) model is used, and the compression matrix in PCA is generated from the predicted channels. By the computer simulation, it is shown that the system capacity is increased by generating the compression matrix from the predicted channel that improves the accuracy of channel restoration.

Interference Alignment (IA) in heterogeneous networks (HetNets) is a promising technique that improves the spectral efficiency significantly. We showed in [1] that transmit antennas at pico BSs could be utilized more efficiently by clustering pico cells in IA in HetNet where the clustering formation was optimized so as to minimize the rate loss caused by inter-cluster interference. In [1], the optimum clustering formation was selected by comparing all possible formations, that is, the value of the objective function for all possible formations was calculated. Therefore, we required the enormous complexity to construct pico cell clusters. In this paper, we propose a novel algorithm for clustering pico cells that reduces the complexity of the clustering process. In particular, we define rate-loss matrix that represents the rate loss caused by inter-pico interference, and translate the optimization problem to the construction of rate-loss matrix. Clearly, the proposed algorithm is sub-optimum in terms of achievable rate compared to all-search algorithm used in [1]. However, the simulation results show that the difference of achievable rate between the proposed algorithm and all-search algorithm is negligible and becomes smaller as the cluster size decreases. We evaluate the complexity of proposed algorithm quantitatively comparing to all-search algorithm, and show that our algorithm reduces the complexity of clustering process significantly while achieving almost same performance.

In interference alignment (IA), interference signals are aligned in a certain signal subspace of each receiver using transmit weights and eliminated using receive weights. The transmit and receive weights are typically calculated at transmitters with an iterative optimization based on estimated channel state information (CSI) from receivers. However, the estimated CSI is different from the channel when a signal is transmitted by the transmitter, because the channel varies during a CSI feedback and the iterative weight calculation. In IA with the weights based on the estimated CSI, the interference signals are not perfectly aligned and remains at the receiver. As a result, a rate decreases due to the remaining interference signals. In this paper, we propose three IA methods having the capability of adapting to time-varying channel using auto regressive (AR) model by which next state is predicted with some past states. In the first method, the channel when a signal is transmitted at the transmitter is predicted from the estimated CSI at the receiver based on AR model. Transmit and receive weights are calculated with the predicted CSI so that the interference signals are aligned and eliminated. In the second method, transmit and receive weights are predicted with past transmit and receive weights based on AR model, respectively. In the third method, only the transmit weight is predicted with past transmit weights based on AR model and receive weight is calculated with the estimated CSI. The prediction methods are able to calculate transmit and receive weights with low amount of calculation compared with the iterative optimization. Through computer simulation, compared with general IA methods that do not use prediction process, the proposed ones are shown to improve the rate irrespective of signal to noise power ratio (SNR) and decrease the calculation amount.

Interference alignment (IA) is a technique shown to be able to achieve a significant overall throughput in a μ — user multiple-input multiple-output (MIMO) interference channel (IC). In this paper, we try to modify the conventional IA designs to achieve enhanced sum-rate performance. We jointly design transmit and receive IA filters (precoding and suppression filters) at a central unit (CU) to reduce the channel state information (CSI) feedback/sharing overhead. At the CU, in a one-way iterative strategy (left ‘L’ to right ‘R’, see Fig. 1), the precoding filters are optimized (on side ‘L’) in the direction of the gradient of the sum-rate, and the suppression filters are chosen (on side ‘R’) to minimize the leakage interferences. Then, in a two-way iterative strategy, the proposed scheme alternates between ‘L’ and ‘R’ sides to design the IA filters through the joint sum-rate maximization and interference leakage minimization on each side. Finally, a modified version of the two-way iterative strategy is proposed to design the IA filters on the basis of signal-to-interference-plus-noise ratio (SINR) (instead of the interference minimization) and the sum-rate maximization techniques. Comparing to other conventional IA designs, the proposed designs with provable convergence show a significant sum-rate performance improvement and often outperform other widely used algorithms, as shown in the simulation examples.

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.

Interference alignment (IA) in heterogeneous networks (HetNet) is a promising technology that can achieve a significant increase of network capacity. However, there are some issues on IA in HetNet. One of them is that each pico base station (BS) requires a large number of transmit antennas, because it tries to align all inter-pico interference. In actual environments, the strength of inter-pico interference varies depending on the distance between pico cells, i.e., inter-pico interference from the remote pico cell is much smaller than that from the close one, so that aligning all interference is not necessarily the best. In addition, the residual interference from macro BS that is not eliminated by IA may decrease the capacity. In this paper, we propose 1) to reduce the required number of transmit antennas at pico BS by clustering pico cells based on the strength of inter-pico interference and 2) to apply combined weight, that comprises ZF (zero forcing) weight and MMSE (minimum mean square error) weight, to pico user equipment (UE) so as to mitigate the residual interference that is not eliminated perfectly by IA. Simulation results show that by applying combined weight to pico UEs, the capacity increases compared to the case of applying only ZF weight, and that the average rate per pico cell normalized by the required number of transmit antennas at each pico BS, that is, the rate per transmit antenna can be increased with pico cell clustering, which indicates that each pico BS can utilize its transmit antennas more efficiently.

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.

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.

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.

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.

This paper proposes a novel interference cancellation technique using the concept of a distributed array, for the secondary use of the frequency resources in frequency division duplexing (FDD)-based cellular systems. In order to achieve efficient distributed interference cancellation, the secondary system exploit intervals for periodic training signals transmitted by FDD-time division multiple access (TDMA)-based cellular systems. We show that an efficient interference cancellation can be achieved by introducing helpers that decode the interference and transfer it to the secondary nodes. We take advantage of the high-speed links within the secondary system and devise a data compressing scheme that reduces time for transferring the decoded interference. We clarify the effectiveness of the proposed scheme by using computer simulation. As a parameter, the transmission quality between the helper and secondary nodes is varied. The results indicate that the proposed distributed interference cancellation is effective compared to a conventional adaptive array using a zero forcing algorithm, when the signal to noise ratio between the helper and the target secondary node is greater than 20 dB.

The application of the multiuser multiple-input multiple-output (MIMO) technique with orthogonal frequency division multiplexing (OFDM) signal transmission to future wireless communications systems has attracted much attention. In the uplink, the difference in the arrival timings among the signals from multiple wireless terminals (WTs) causes significant transmission quality degradation. In this paper, we propose a novel multiuser detection (MUD) technique that overcomes this problem. The proposed method discards the samples at both ends of each fast Fourier transform (FFT) window. In addition, it overlaps the FFT windows to recover all consecutive samples. Thus, it enables a signal separation regardless of the difference in the arrival times among WTs with an acceptable level of calculation complexity. The achievable bit error rate (BER) performance of the proposed MUD method is evaluated by computer simulation in a frequency selective fading channel. The results show that compared to the conventional method the proposed MUD method reduces the required Eb/N0by 2.6 dB to achieve the BER of 10-4, when 16QAM and rate 2/3 convolutional codes are used.

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.

Single-carrier (SC) transmission with frequency- domain equalization (FDE) is expected to expand the service area of wireless communication systems by taking advantage of its lower peak-to-average power ratio (PAPR) compared to when using orthogonal frequency division multiplexing (OFDM). In the uplink, the transmit timing difference among the signals from multiple mobile terminals (MTs) becomes significant in a large service area. If the difference in arrival timing among MTs exceeds the cyclic prefix (CP) length, it is difficult to separate the signal of each MT and the transmission quality is severely degraded. In this paper, we propose a novel multiuser detection (MUD) technique that can separate the signals regardless of the difference in the arrival timing between MTs with an acceptable level of calculation complexity. The achievable bit error rate (BER) performance is evaluated by computer simulation in a frequency selective fading channel and the results confirm the effectiveness of the proposed method.

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.

In this paper, the possibility for a short-range cognitive radio (secondary communication system) to be located within the service area of the primary system is discussed. Although the secondary system interferes with the primary system, there can be certain locations in the service area of the primary system where the cognitive radio can reuse the frequency of the primary system without disturbing it or being disturbed by the primary system. We say that in those locations there is a spatial opportunity for communication in the secondary system. The primary and secondary systems have different features and usage scenarios, which result in different deployment conditions. This gives rise to differences between the path loss for a link in the primary system and the path loss for the interference from the primary to secondary systems. This paper investigates the impact of the heterogeneous path loss conditions on the spatial opportunity for the secondary system. Our approach can be applied as a general way to investigate the coexistence among multiple systems. We clarify that the change of the path loss coefficient due to the different antenna heights of the primary and secondary systems can largely affect the spatial opportunity for frequency reuse by the cognitive radios.

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.

This paper presents the experimental results of a testbed for a new SDMA system whose base stations employ multibeam antennas and space time block codes (STBC). New channel response estimation circuits are developed to estimate channel response with high accuracy at terminals and are implemented into the testbed. To evaluate the effectiveness of the system and the testbed, measurements are performed using the testbed in an anechoic chamber. The measurement results confirm that the proposed SDMA system can reduce the influence of the multipath fading effects and the new channel estimation circuit works well.

Space division multiple access (SDMA) is an attractive candidate for increasing the channel capacity. In an SDMA system, signals from multiple terminals are spatially separated and this feature enables terminals to use the same frequency and the same timing. However, even slight channel estimation error significantly affects the transmission quality in the downlink. We propose a new closed-loop SDMA downlink beam-forming method that employs transmission diversity for multi-beams and mitigates the influence of multi-path fading. The effectiveness with respect to angular spread and the Doppler frequency is evaluated in computer simulations and the proposed method is shown to achieve an signal-to interference-plus-noise-ratio (SINR) that is 3-dB higher than that of the conventional scheme in a flat fading environment.