Low Earth Orbit (LEO) satellite constellation systems are expected to be integrated with terrestrial networks for future communication services over wide areas. For real-time applications, direct data relay between users is effective to minimize the propagation delay via LEO satellites, where multi-beam control is an essential function in LEO satellites to efficiently allocate beam resources over the areas. However, no previous works considered the satellite’s buffer limitations and the differences in throughput between uplink (UL) and downlink (DL) which caused queuing delays. This paper proposes a coordinated UL/DL multi-beam scheduling to minimize the end-to-end latency of data relay by considering the available buffer space of the satellite and variations in UL/DL throughput due to satellite movement. An optimization problem is formulated for the scheduling design to minimize the end-to-end latency. We demonstrate with simulation results that the proposed scheduling method reduces the latency by 16.0%, compared to the conventional methods.

We consider a new paradigm for a user-centric network in millimeter-wave (mmWave) backhaul systems designed for high-speed vehicles. By deploying distributed MIMO along the travel routes of high-speed vehicles, inter-cell handovers and interference can be reduced while enhancing communication capacity by leveraging the diversity gain between distributed antennas (DA). However, challenges arise regarding the abrupt changes in Doppler frequency offset (DFO) and timing offset (TO) during DA switching. Frequent DA switching in high-speed environments degrades the demodulation performance. To address these issues, we propose a novel DFO and TO pre-compensation for seamless DA switching. The proposed method effectively estimates and pre-compensates DFO and TO by leveraging existing reference signal. Since the abrupt changes in DFO and TO are suppressed during switching, seamless switching can be achieved without requiring mobile terminal synchronization for each DA. Simulations demonstrate that the proposed method suppresses degradation of demodulation performance over a 160-millisecond interval, ensuring seamless DA switching.

This paper presents a novel wireless autoencoder (WAE) consisting of neural network (NN) modulator and probabilistic data association detector with the aid of deep unfolding (PDA-DU) in coded multi-input multi-output (MIMO) systems. When using error correction code (ECC), the signal constellation that maximizes the minimum Euclidean distance, such as quadrature amplitude modulation (QAM), is not always optimal. For example, set partitioning is often utilized in higher-order modulation schemes. On the other hand, nonlinear iterative signal detection of PDA has been shown to efficiently suppress interference and to reduce the bit error rate (BER) compared to traditional minimum mean square error (MMSE) spatial filtering. However, PDA may degrade detection performance due to outliers in the expected value of transmitted signals. To solve these complicated issues, the proposed WAE retrieves the best signal constellation using the NN modulator, and simultaneously mitigates outliers in the expected value by introducing scaling factors optimized by DU in the PDA detector.

This paper proposes a new site-specific codebook design method for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to avoid unnecessary time and power consumption during beam search. In order to achieve a highly practical beam codebook, the proposed method utilizes reference signal received power (RSRP) regularly reported by users and pre-calculated transmit beamforming gains in predefined directions to estimate the dominant path directions of the users. The beam directions in the codebook are determined by the path direction estimates after applying K-means clustering. Simulation results show that the proposed method can accurately estimate the dominant path directions of users and improve received power and throughput performance compared to a conventional method. The proposed method can reduce beam search overhead by about 33% compared with the conventional method when achieving a throughput of 2.3 Gbps.

This paper proposes a novel vector similarity search (VSS)-based modulation and coding scheme (MCS) selection for massive multi-user multiple-input multiple-output orthogonal frequency division multiplexing (MU-MIMO-OFDM) systems that employ uplink multi-user detection (MUD) based on iterative signal estimation. To maximize the uplink throughput of MU-MIMO-OFDM systems, it is necessary to assign a carefully selected MCS to each user according to an accurate prediction of the mutual information (MI) that can be achieved with MUD based on the knowledge of the estimated channel state information (CSI). However, since the detection accuracy of iterative MUDs, such as expectation propagation (EP), varies depending on the convergence characteristics, it is challenging to analytically predict the achievable MI in the presence of MUD. To address this difficulty, we propose a novel method for predicting the achievable MI by creating a vector database (VDB) offline that stores feature vectors (keys) computed from CSI and the actual MI (values) achieved with iterative MUD, and then searching this VDB online using approximate nearest neighbors (ANN) search, which enables VSS at ultra-high speed. Simulation results show that the MCS selection based on the proposed MI prediction achieves higher uplink throughput than the conventional schemes in MU-MIMO-OFDM systems using the EP-based MUD.

This paper presents a novel radio resource allocation method for uplink scalable mmWave distributed MIMO (D-MIMO). The user-cluster-centric (UCC) partial minimum mean square error combining ($\mathrm{P}-\mathrm{MMSE}$) is a scalable reception scheme in large D-MIMO. However, the cluster-wise operation of the UCC approach causes performance degradation due to high intra- and inter-cluster interferences under high-user-density environments. In order to suppress the occurrence of both types of interferences, we propose an integrated radio resource and cluster allocation method that maximizes the performance of UCC-P-MMSE. The proposed method enables efficient suppression of the interference using two adjustable thresholds considering intra- and inter-cluster interferences. System-level simulations assuming an uplink mmWave D-MIMO system demonstrate that the proposed method improves a 5% -tile user throughput by $\mathrm{6 0 \%}$ compared to a conventional UCC-P-MMSE by avoiding the significant interference.

This paper proposes an outer loop link adaptation (OLLA) algorithm for massive multi-user multi-input multi-output (MIMO) systems that employs uplink multi-user detection (MUD) based on generalized approximate message passing (GAMP). The contribution aims to improve uplink system throughput performance for future beyond-fifth-generation mo-bile communication systems by designing a novel scheduler that can select spatially multiplexed user equipment (UE) devices and their modulation and coding schemes (MCSs), considering the high detection accuracy provided by the GAMP-based MUD. To achieve this, we propose an OLLA algorithm that accu-rately predicts the signal-to-interference and noise power ratio (SINR) that each UE can achieve after the GAMP-based MUD. Specifically, the proposed method can dynamically optimize the scheduler according to the iterative detection characteristics of GAMP by introducing a mechanism to correct the predicted SINR separately for each combination of spatially multiplexed UEs. System-level simulation results indicate that adjusting our OLLA algorithm achieves a 50% higher throughput than the conventional OLLA algorithm when applied to GAMP.

Millimeter wave (mmWave) distributed multiple-input multiple-output (MIMO), which is also known as cell-free massive MIMO, is a promising technology for beyond 5G. To improve system capacity in mmWave distributed MIMO systems, a coordinated scheme between distributed access points (APs) is required. In this paper, we evaluate the throughput performance of precoding across multiple APs under channel aging and compare it with that of inter-AP coordinated user and beam selection to clarify the inter-AP coordinated scheme suitable for practical mmWave distributed MIMO systems. Simulation results show that the inter-AP coordinated user and beam selection achieves a higher throughput performance than the precoding. Moreover, to make the inter-AP coordinated user and beam selection more practical, we propose a coordinated selection method using reference signal received power (RSRP) database. Simulation results show that the proposed method improves the 5%-tile user throughput by 17% compared to the coordinated selection without using the RSRP database.

A new concept of a radio-over-fiber (RoF) system, namely, “delta-sigma RoF embedding a pulse-distortion model (PDM),” for beyond fifth-generation mobile-communication systems is proposed. The PDM is generated by a distorted baseband feedback signal. To implement the proposed concept, an all-digital transmitter based on modified low-pass delta-sigma modulation, a simple PDM in the RoF channel, and a method for optimizing the parameters of the model are also proposed. An experimental demonstration using a l-GHz-bandwidth OFDM signal showed that the proposed delta-sigma RoF system improved in-band signal-to-noise ratio (SNR) by 6 dB compared to that of a conventional delta-sigma RoF system and achieved in-band SNR of 30.7 dB after 23.6-Gbps fiber transmission.

We propose two full-digital receive beamforming (BF) methods for low-complexity and high-accuracy uplink signal detection via Gaussian belief propagation (GaBP) at base stations (BSs) adopting massive multi-input multi-output for open radio access network. In such scenarios, it is vital to reduce the cost of the BSs by limiting the bandwidth of fronthaul (FH) links, and the dimensionality reduction of the received signal based on receive BF at a radio unit is a well-known strategy to reduce the amount of data transported via the FH links. We clarify appropriate criteria for designing a BF weight considering the subsequent GaBP signal detection with the proposed methods: singular-value-decomposition-based BF and QR decomposition-based BF with the aid of discrete-Fourier-transformation-based spreading. Both methods enable dimensionality reduction without compromising the desired signal power by taking advantage of a null space of the channels. BF reduces correlations between the received signals in the BF domain, which improves the robustness of GaBP against spatial fading correlation. Simulation results indicate that the proposed methods improve detection capability while significantly reducing computation.

We consider large multi-user detection (MUD) via deep unfolding-aided belief propagation (BP) in coded multi-user MIMO (MU-MIMO) systems. A BP detector optimized (trained) by data-driven-tuning of embedded internal parameters achieves low-complexity and high-accuracy MUD while compensating practical imperfections. However, in actual implementation, these parameters should be optimized according to system parameters, e.g., modulation and coding scheme (MCS). In particular, when channel coding is used, it is vital not only to minimize the mean square error (MSE) but also to enhance the Gaussianity of the output log-likelihood ratio (LLR), in order to maximize the error correction capability of the subsequent soft-decision decoder. To that end, a novel loss function based on a weighted average of negentropy, which is a key measure to evaluate the Gaussianity, and MSE of the detector output is proposed. Simulation results show that the trainable Gaussian BP (T-GaBP) detector optimized with the proposed negentropy-aware loss function significantly improves the bit error rate (BER) performance of the decoder output and substantially outperforms the T-GaBP optimized with the typical MSE loss function.

This paper proposes a deep unfolding-aided belief propagation (BP) for large multi-user multi-input multi-output (MU-MIMO) detection under correlated fading channels. A BP-based detector is a well-known strategy for realizing large-scale MU detection (MUD) with low-complexity and high-accuracy. However, its convergence property is severely degraded under insufficient large-system conditions and spatial fading correlation among RX antenna elements. To compensate for this drawback, we design a trainable Gaussian BP (T-GaBP) having well-organized trainable internal parameters based on the BP structure. These parameters are optimized by the deep learning techniques in the signal-flow graph of unfolded GaBP; this approach is referred to as data-driven tuning. By training the parameters according to the system model, T-GaBP can maintain the high detection capability even in practical system configurations that differ from the ideal uncorrelated massive MIMO assumption. Numerical results show that the proposed detector improves the convergence property and achieves a comparable detection performance to the cutting-edge expectation propagation (EP) detector in correlated MUD, with a lower computational cost.

To enhance 5G (5G Evolution) further, more stable high bit-rate transmission using high SHF bands including 28 GHz or EHF bands is provided even in high-mobility environments such as in high-speed trains. However, in future 5G use cases in the Shinkansen, a dynamic changes in the beam direction and a greater Doppler frequency shift may cause performance degradation in 28 GHz-band transmission. Thus, we conduct a 28-GHz band 5G experimental trial on an actual Shinkansen that runs at a maximum speed of 283 km/h. This paper introduces the world's first 28 GHz band 5G experimental system used in a 5G experimental trial in the Shinkansen travelling over 200 km/h. We detail the experimental configuration in which three base stations are deployed along the Tokaido Shinkansen railway with a mobile station located in the train. This paper also presents the downlink transmission performance measured in such a high-mobility environment. The experimental trial can achieve throughput exceeding 1.0 Gbps and consecutive handovers among the three BSs.

The fifth-generation (5G) mobile communication system introduces the use of high frequency bands with very wide bandwidth for realizing super high bit rate more than 20 Gbps. To use such high frequency bands, Massive MIMO technologies by hybrid beamforming is a promising technology in terms of power consumption and implementation cost. Experimental trials of hybrid beamforming in 28 GHz band were already conducted in an indoor anechoic chamber and in an outdoor light-of-sight (LOS) environment. However, as MIMO takes advantage of multipath, it is important to evaluate the system performance in multipath-rich environment. It is also important to evaluate the system performance in non LOS (NLOS) environment, because high straightness of high SHF band can degrade its performance. To clarify these actual impacts, in multipath-rich room with some obstacles, we conducted two indoor downlink experimental trials: SU-MIMO and 8-user MU-MIMO at 28 GHz frequency band with 500 MHz bandwidth. The results of SU-MIMO experiments show more than 20 Gbps user throughput was achieved in the multipath-rich environment. We also confirmed that strong reflector improves the throughput when a user is in the NLOS environment. MU-MIMO experiments show the hybrid beamforming can improve both the system throughput and user fairness compared to analog beamforming.

This paper focuses on multi-user detection in uplink Massive multiple-input multiple-output (MIMO) systems. As a low computational complexity signal detection algorithm, matched filter (MF)-based Gaussian belief propagation (MF-GaBP) using adaptively scaled belief (ASB) has been proposed. In addition, to suppress the negative impact of spatial correlation, an MMSE detector is introduced as the prior stage processing of MF-GaBP. Through computer simulations, this algorithm exhibits high performance even in high- spatially loaded Massive MIMO scenarios. However, in actual radio environments, there is a possibility to observe an extremely high channel correlation among some antenna elements or some users due to co-located polarization antenna elements or closely deployed mobile stations (MS). The highly correlated channel results in the performance degradation of MF-GaBP using ASB because of the low reliability of initial beliefs. This paper aims to verify the performances of MF- GaBP in the actual radio environments. The predetermined parameters in MF- GaBP are also adjusted according to actual environments for improving the convergence property of the iterative detection. To obtain propagation channel data for performance evaluation in the actual environments, an indoor experimental trial using Massive MMO equipment with 32 antenna elements was carried out under a condition that 16 MSs are closely deployed. Computer simulations using the obtained propagation channel data validate that simply applying the MMSE detector before MF-GaBP with ASB suppress an error floor in the bit error rate (BER) performance even in highly correlated channel.

The fifth-generation (5G) mobile communication system has attracted much attention to provide various kinds of services and applications for 2020 and later. To verify the feasibility of high presence public viewing service, the world's first maritime 5G experiment is conducted in Windsurfing World Cup 2018. In the experiment, 5G is used for 4K video transmission from a ship to public viewing area at land. This paper briefly reports that downlink and uplink maritime transmission performance of 5G employing 28 GHz band Massive MIMO (Multiple Input Multiple Output) and the success of the public viewing trial.

Field trials of the fifth-generation (5G) mobile communication system using 28 GHz band at which almost 1-GHz band will be available have been performed all over the world. To realize large coverage at such a high frequency band, beamforming by Massive MIMO (Multiple Input Multiple Output) is necessary to compensate large path loss. Furthermore, beam tracking which adaptively changes beam direction according to user location is an important function to support user mobility. Authors already conducted high mobility trials at a velocity of 150 km/h using 28 GHz-band 5G prototypes implementing beam tracking. To support ultra-high mobility on a bullet train, in April 2018, we conducted outdoor trials using the specially customized vehicle with the velocity of up to 300 km/h. This paper reveals the latest downlink performance results of the trial.

Massive multiple-input multiple-output (MIMO) is a promising technology for 5G systems and is expected to improve the performance of multi-user MIMO (MU-MIMO). When uplink (UL) channel estimation results are used for downlink (DL) MU-MIMO precoding, the UL channel estimation errors degrade the performance of DL MU-MIMO. To reduce the estimation errors, a channel estimation method using the channel sparsity in beam space has been studied. This method, which is called beam space channel estimation (BSCE) in this paper, can reduce the estimation errors by setting the channel estimates of non-dominant beams to zeros. However, when the directions of beams are not close to those of dominant paths, BSCE cannot reduce the estimation errors sufficiently. In this paper, we propose a BSCE using multiple discrete Fourier transform (DFT) matrices which form beams in mutually different directions to increase the probability that the directions of beams are close to those of dominant paths. We also propose a non-zero beam selection method to prevent the directions of nonzero beams from being limited to a specific angular range. Simulation results show that the proposed method using four DFT matrices improves cell throughput performance by 53% compared with not using BSCE and by 24% compared with the conventional BSCE when the signal-to-noise ratio (SNR) of the UL reference signal is 0 dB.

Device-to-device (D2D) discovery is an important function for public safety long term evolution (PS-LTE) to detect user equipment (UE) located in proximity. When many UEs are gathered at a disaster site or an incident command post, in-band emission (IBE) of discovery signals can cause significant interference resulting in discovery errors. In order to reduce the effect of IBE, a radio resource mapping method is proposed for autonomous D2D discovery in a PS-LTE system configured to repeatedly transmit discovery signals over different subframes. The proposed method interleaves discovery resources so that retransmission on resources with a certain frequency gap follows transmission on adjacent resources. Simulation results revealed that the proposed method takes 10% less time to detect UEs than a conventional method does.

Public safety networks are desired to meet increasing demands for sharing images or videos among first responders and incident commanders. Long term evolution (LTE) is considered a candidate to achieve such broadband services. However, in the case of a disaster or a big sporting event, the traffic suddenly increases sharply as the number of active public safety users increases. This paper reports methods for allocating radio resources for device-to-device (D2D) communication as a means to enhance system capacity of the public safety LTE (PS-LTE). The methods allow spatial reuse of the resources among D2D communicating public safety users satisfying certain sharing conditions. Two practical scheduling algorithms that take into account both spatial reuse of the resources and fairness between the D2D communicating users are described. System level simulation results reveal that the methods can increase the number of users that satisfy the throughput requirement for video transmission.

Joint signal detection and channel estimation based on the expectation-maximization (EM) algorithm has been investigated for orthogonal frequency-division multiplexing (OFDM) mobile communications over fast- fading channels. In order to make the signal detection suitable for the iterative EM-based receiver with multiple antennas, this paper proposes spatial removal from the perspective of a message- passing algorithm on factor graphs. The spatial removal performs the channel estimation of a targeted antenna by using detected signals that are obtained from the received signals of all antennas other than the targeted antenna. It can avoid the repetitive use of unreliable received signals for consecutive signal detection and channel estimation. Computer simulations under fast-fading conditions demonstrate that appropriate application of the spatial removal can exploit such a removal effect and the spatial diversity, resulting in improvement of the packet error rate (PER).

To maintain good transmission performance of orthogonal frequency-division multiplexing (OFDM) mobile communications over fast-fading channels, iterative maximum a posteriori (MAP) receivers that iterate channel estimation and signal detection on the basis of the expectation-maximization (EM) algorithm have been investigated. In order to make such a MAP receiver more robust against time-variant channels, this paper derives channel estimation with subcarrier removal from the perspective of a message-passing algorithm on factor graphs. The subcarrier removal estimates the channel frequency response by using the detected signals of all subcarriers other than that of a targeted subcarrier. This modification can avoid the repetitive use of incorrectly detected signals for the channel estimation. Computer simulations under fast-fading conditions demonstrate that the EM-based MAP receiver performing the subcarrier removal can improve the packet error rate (PER) drastically.

In this paper, we propose novel criterion and novel power control schemes based on the capacity conservation ratio (CCR) by taking the Rayleigh fading effect into account using theoretical analysis. The CCR is defined as the ratio of the decreased capacity in the spectrum sharing to the original capacity of a system. We utilize the CCR as a novel metric for protecting a primary system (PS) from large capacity degradation. The proposed power control schemes based on CCR are divided into two kinds of power control scheme; an exact power control scheme and a simplified power control scheme. We analytically evaluate the performance of the simplified power control scheme and compare it with that of the other. We find that the simplified power control scheme offers sufficient PS protection performance without high computational cost.

This paper proposes a new channel estimation method for the expectation-maximization (EM) algorithm applied to an OFDM receiver. Conventional schemes using the EM algorithm assume the random walk model as a process equation of a channel impulse response, and thus have insufficient tracking ability to fading fluctuation over fast fading channels. To improve the tracking ability, the proposed channel estimation method employs the differential model having the first and higher order differentials of the channel impulse response with respect to time. Computer simulations under the fast fading condition demonstrate that the proposed method is much superior in packet error rate to the conventional schemes employing the random walk model.