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 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.

In this paper, we summarize our 5G R&D achievements for "High-Data-Rate and Low-Power-Consumption Radio Access Technologies with Ultra Higher-Frequency-Band and Wider-Bandwidth Massive MIMO", funded by the Ministry of Internal Affairs and Communications (MIC), Japan. This Japanese national project consists of three research subjects: "Massive MIMO (massive-element antenna) and beam control technologies for low SHF bands", "Wideband Massive MIMO and beam control technologies for high SHF bands" and "Discovery technologies for terminals with ultra-low power consumption". We describe detailed research contents and achievements of our promoting 5G R&D project.

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.

Massive MIMO and dense base station (BS) deployment are promising technologies to achieve large system capacity that is one requirement for 5G mobile communications systems. To mitigate inter-BS interference which is increased by dense BS deployment, coordinated beamforming (CB) has been studied. A low complexity CB scheme is preferable especially for massive MIMO system because the complexity of beamforming weight calculation depends on the number of antennas. ZF and MMSE based CB schemes have been investigated as the low complexity scheme, while the throughput performances of these schemes for massive MIMO system have not been compared. In this paper, we evaluate the throughput performances of ZF and MMSE based CB schemes while changing the number of spatially multiplexed users and interference suppressed users. Simulation results show that MMSE based scheme achieves about 6% higher throughput performance than ZF based scheme without the appropriate selection of interference suppressed users and is suitable for massive MIMO system.

Massive MIMO is a promising technology for the 5G mobile communications system and contributes to the enhancement of multi-user MIMO transmission. However, the user scheduling of multi-user MIMO transmission requires a high computational complexity because the matrix operation is performed to estimate SINR, which is used as a user selection criterion. In this paper, we investigate low complexity multi-user MIMO scheduling schemes that estimate the SINR without the matrix operation. The schemes are the polynomial approximation scheme, which approximately estimates the SINR by channel correlation between users, and the single-user based scheme, which uses the SINR of single-user transmission instead of the SINR of multi-user MIMO transmission. Simulation results show that the complexity of the polynomial approximation scheme is reduced to a few tenths of that of the matrix operation based scheme without throughput degradation. It is also shown that the complexity of the single-user based scheme is reduced to a few thousandths of that of the matrix operation based scheme with little throughput degradation as the number of spatially multiplexed users increases.

There are many requirements for increasing the user capacity in radio wireless communication. Adaptive array antennas are useful for reducing the interference signal. This paper proposes multiuser equalization based on an adaptive array antenna for DS/CDMA. This approach is considered as the joint transmitter and receiver multiuser equalizer in spatial and temporal domains. First, this paper describes the channel model for DS/CDMA in spatial and temporal domains. Then, the proposed system is described. Finally the system is evaluated by computer simulations.

This paper describes spatial and temporal modeling of a multipath channel which is useful in array antenna environment for mobile radio communications. From this modeling, a no distortion condition that is the Nyquist theorem, is derived for total equalization in both spatial and temporal domains. We use an adaptive tapped-delay-line array antenna as a tool of equalization in spatial and temporal domains. Several criteria for such spatial and temporal equalization are available to update weight and tap coefficients such as ZF (zero forcing), MSE (mean square error) and so on. In this paper, we discuss the optimum weight based on ZF criteria in spatial and temporal domains. Moreover, computer simulations show the performance of an adaptive array antenna based on the ZF criteria in spatial and temporal domains.