We evaluated the transmission performance, including received power and transmission throughput characteristics, in 4×4 single-user multiple-input multiple-output (SU-MIMO) transmission for synchronous time division duplex (TDD) and downlink data channels in comparison with single-input single-output (SISO) transmission in an environment where a local 5G wireless base station was installed on the roof of a research building at our university. Accordingly, for the received power characteristics, the difference between the simulation value, which was based on the ray tracing method, and the experimental value at 32 points in the area was within a maximum difference of approximately 10 dB, and sufficient compliance was obtained. Regarding the transmission throughput versus received power characteristics, after showing a simulation method for evaluating throughput characteristics in MIMO, we compared the results with experimental results. The cumulative distribution function (CDF) of the transmission throughput shows that, at a CDF of 50%, in SISO transmission, the simulated value is approximately 115Mbps, and the experimental value is 105Mbps, within a difference of approximately 10Mbps. By contrast, in MIMO transmission, the simulation value is 380Mbps, and the experimental value is approximately 420Mbps, which is a difference of approximately 40Mbps. It was shown that the received power and transmission throughput characteristics can be predicted with sufficient accuracy by obtaining the delay profile and the system model at each reception point using the both ray tracing and MIMO simulation methods in actual environments.

Channel state information (CSI) acquisition at the transmitter side is a major challenge in massive MIMO systems for enabling high-efficiency transmissions. To address this issue, various CSI feedback schemes have been proposed, including limited feedback schemes with codebook-based vector quantization and explicit channel matrix feedback. Owing to the limitations of feedback channel capacity, a common issue in these schemes is the efficient representation of the CSI with a limited number of bits at the receiver side, and its accurate reconstruction based on the feedback bits from the receiver at the transmitter side. Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6%-26% compared with DL-based receivers and 33%-58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.

A novel analog decoding method using only 90-degree phase shifters is proposed to simplify the decoding method for short-range multiple-input multiple-output (MIMO) transmission. In a short-range MIMO transmission, an optimal element spacing that maximizes the channel capacity exists for a given transmit distance between the transmitter and receiver. We focus on the fact that the weight matrix by zero forcing (ZF) at the optimal element spacing can be obtained by using dividers and 90-degree phase shifters because it can be expressed by a unitary matrix. The channel capacity by the proposed method is next derived for the evaluation of the exact limitation of the channel capacity. Moreover, it is shown that an optimal weight when using directional antennas can be expressed by using only dividers, 90-degree phase shifters, and attenuators, regardless of the beam width of the directional antenna. Finally, bit error rate and channel capacity evaluations by both simulation and measurement confirm the effectiveness of the proposed method.

In this paper, performances of two different virtual multiple-input multiple-output (MIMO) transmission schemes — spatial multiplexing (SM) and space-time block coding (STBC) — in a correlated wireless sensor network are analyzed. By utilizing a complex Wishart distribution, we investigate the statistical properties of a correlated virtual MIMO channel between the sensors and data collector that is used in the performance analysis of each MIMO transmission mode. Distributed sensors then transmit their data cooperatively to the data collector by choosing a proper transmission mode adaptively based on the channel conditions and spatial correlation among the sensors. Furthermore, after analyzing the energy efficiencies of SM and STBC, we propose a new energy efficient mode switching rule between SM and STBC. Finally, by analytically deriving the required transmit energy of the proposed adaptive transmission scheme, the manner in which the spatial correlation influences the energy consumption is shown. This suggests a cooperating node scheduling protocol that makes energy consumption less sensitive to the variation of the spatial correlation.

Traditional cellular networks suffer the so-called “cell-edge problem” in which the user throughput is deteriorated because of pathloss and inter-cell (co-channel) interference. Recently, Base Station Cooperation (BSC) was proposed as a solution to the cell-edge problem by alleviating the interference and improving diversity and multiplexing gains at the cell-edge. However, it has minimal impact on cell-inner users and increases the complexity of the network. Moreover, static clustering, which fixes the cooperating cells, suffers from inter-cluster interference at the cluster-edge. In this paper, dynamic fractional cooperation is proposed to realize dynamic clustering in a shared RRU network. In the proposed algorithm, base station cooperation is performed dynamically at cell edges for throughput improvement of users located in these areas. To realize such base station cooperation in large scale cellular networks, coordinated scheduling and distributed dynamic cooperation are introduced. The introduction of coordinated scheduling in BSC multi-user MIMO not only maximizes the performance of BSC for cell-edge users but also reduces computational complexity by performing simple single-cell MIMO for cell-inner users. Furthermore, the proposed dynamic clustering employing shared RRU network realizes efficient transmission at all cell edges by forming cooperative cells dynamically with minimal network complexity. Owing to the combinations of the proposed algorithms, dynamic fractional cooperation achieves high network performance at all areas in the cellular network. Simulation results show that the cell-average and the 5% cell-edge user throughput can be significantly increased in practical cellular network scenarios.