Reconfigurable intelligent surface (RIS) has emerged as a cost-and energy-efficient solution to enhance the wireless communications capacity. However, recent studies show that a very large surface is required for a RIS-assisted communications system; otherwise, they may be outperformed by the conventional relay. Furthermore, the performance gain of a RIS can be considerably degraded by hardware impairments such as limited-resolution phase shifters. To overcome those challenges, we propose a hybrid relay-reflecting intelligent surface (HR-RIS) architecture, in which a single or few elements serve as active relays, while the remaining only reflect the incident signals. We propose two HR-RIS architectures, namely, the fixed and dynamic HR-RIS. The joint transmit beamforming at the base station and hybrid relay-reflecting at the HR-RIS are designed based on alternating optimization and successive convex approximation methods. The simulation results for a 4 × 2 multiple-input multiple-output system show that the proposed HR-RIS with only a single active element can achieve about 40% and 25% improvement in spectral efficiency and energy efficiency, respectively, with respect to the conventional RIS-aided system. The results also show that the HR-RIS can outperform the conventional relaying scheme in most of the considered scenarios.

In this study, we consider the application of deep learning (DL) to tabu search (TS) detection in large multiple-input multiple-output (MIMO) systems. First, we propose a deep neural network architecture for symbol detection, termed the fast-convergence sparsely connected detection network (FS-Net), which is obtained by optimizing the prior detection networks called DetNet and ScNet. Then, we propose the DL-aided TS algorithm, in which the initial solution is approximated by the proposed FS-Net.Furthermore, in this algorithm, an adaptive early termination algorithm and a modified searching process are performed based on the predicted approximation error, which is determined from the FS-Net-based initial solution, so that the optimal solution can be reached earlier. The simulation results show that the proposed algorithm achieves approximately 90% complexity reduction for a 32 × 32 MIMO system with QPSK with respect to the existing TS algorithms, while maintaining almost the same performance. Index TermsMIMO, deep learning, deep neural network, tabu search.

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In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multipleinput-multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to predict the minimum metrics of the "deep" paths in subtrees before commencing the tree search in SD. To reduce the complexity of the NN, we employ the input vector with a reduced dimension rather than using the original received signals and full channel matrix. The outputs of the NN, i.e., the predicted minimum path metrics, are exploited to determine the search order between the sub-trees, as well as to optimize the initial search radius, which may reduce the computational complexity of SD. For further complexity reduction, an early termination scheme based on the predicted minimum path metrics is also proposed. Our simulation results show that the proposed DPP-SD scheme provides a significant reduction in computational complexity compared with the conventional SD algorithm, despite achieving near-optimal performance. Index TermsMIMO, sphere decoding, tree search, machine learning, deep learning, neural network.The authors are with the 2 interest [2]. To maximize the achievable data rates in large MIMO systems, the base station needs to receive as many symbols as possible simultaneously from multiple terminals, which leads to enhanced multiplexing gains. In this circumstance, a near-optimal receiver like SD plays an important role in approaching the channel capacity. However, the complexity of SD significantly increases with the number of antennas [3], which makes it difficult to apply to large MIMO systems.Recently, deep-learning (DL) techniques have been applied in various fields, exhibiting eminent performance. Motivated by the performance of DL technologies in other fields, there have been attempts to apply DL to MIMO detection [4]-[8]. In particular, the DL-based sphere decoding (DL-SD) algorithm is derived to choose the optimal hypersphere radius [4]. In addition, a deep network architecture, called DetNet, is proposed to estimate the solution of MIMO detection [5]. Furthermore, the sparsely connected neural network (ScNet) is developed to simplify the structure of DetNet for massive MIMO systems [6]. The application of a deep neural network to reduce the computational complexity of the conventional belief propagation detector is proposed in [7], and the orthogonal approximate message-passing network (OAMP-Net) architecture is proposed to improve the performance of the iterative detection algorithm with trainable variables [8].In this paper, a novel learning-aided SD algorithm is proposed. The main idea of the proposed algorithm is to predict the minimum path metric among "deep" paths of each sub-tree in a large tree structure by using a neural network (NN). In large MIMO systems, the required information to estimate the path metrics can have large dimension, which can significantly increase the complexity of the NN. To resolve this problem, the size of the input vector to...

Abstract-This paper proposes two types of new decoding algorithms for a network coding aided relaying (NCR) system, which adopts multiple antennas at both the transmitter and receiver. In the NCR system, the relay station (RS) decodes the data received from both the base station (BS) as well as from the mobile station (MS) and combines the decoded signals into a single data stream before forwarding it to both. In this paper, we consider the realistic scenario of encountering decoding errors at the RS, which results in erroneous forwarded data. Under this assumption, we derive decoding algorithms for both the BS and the MS in order to reduce the deleterious effects of imperfect decoding at the RS. We first propose a decoding algorithm for a hard decision based forwarding (HDF) system. Then, for the sake of achieving further performance improvements, we also employ soft decision forwarding (SDF) and propose a novel error model, which divides the error pattern into two components: hard and soft errors. Given this error model, we then modify the HDF decoder for employment in SDF systems. We also derive estimation algorithms for their parameters that are required for the efficient operation of the proposed decoders. Our simulation results show that the proposed algorithms provide substantial performance improvements in terms of the attainable packet error rate as a benefit of our more accurate error model. Index Terms-Network coding, relaying, cooperative communication, multiple-input multiple-output (MIMO) system.