Search

In this work, we investigate hybrid analog–digital beamforming (HBF) architectures for uplink cell-free (CF) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. We first propose two HBF schemes, namely, decentralized HBF (D-HBF) and semi-centralized HBF (SC-HBF). In the former, both the digital and analog beamformers are generated independently at each AP based on the local channel state information (CSI). In contrast, in the latter, only the digital beamformer is obtained locally at the access point (AP), whereas the analog beamforming matrix is generated at the central processing unit (CPU) based on the global CSI received from all APs. We show that the analog beamformers generated in these two HBF schemes provide approximately the same achievable rates despite the lower complexity of D-HBF and its lack of CSI requirement. Furthermore, to reduce the power consumption, we propose a novel adaptive radio frequency (RF) chain-activation (ARFA) scheme, which dynamically activates/deactivates RF chains and their connected analog-to-digital converters (ADCs) and phase shifters (PSs) at the APs based on the CSI. For the activation of RF chains, low-complexity algorithms are proposed, which can achieve significant improvement in energy efficiency (EE) with only a marginal loss in the total achievable rate. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligentsurface (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 $4 \times 2$ 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. [ABSTRACT FROM AUTHOR]

Link adaptation (LA) is an adaptive transmission technique that determines the modulation and coding scheme (MCS) based on channel-state information. In LA, an accurate estimation of the link performance is required to optimally determine the MCS level. In this correspondence, a high-accuracy machine-learning (ML)-aided link-level performance-prediction method for coded multiple-input–multiple-output (MIMO) systems is proposed. The basic concept of this scheme is to apply the ML model to train the relation between the inputs, such as the channel matrix and signal-to-noise ratio, and the output of the block-error rate (BLER). Specifically, we predict the index of the quantized BLER value using a random forest classifier. The simulation results show that the proposed scheme is able to accurately predict the link performance of MIMO systems and outperforms the conventional link performance-prediction schemes. [ABSTRACT FROM AUTHOR]

In this work, we propose a novel dynamic unequal sub-array (DUESA) architecture for massive multiple-input multiple-output (MIMO) systems with partially connected hybrid beamforming (PC-HBF) that relaxes the antenna-allocation constraint in the conventional unequal sub-array (UESA) architecture. The proposed DUESA architecture dynamically allocates antennas to sub-arrays based on channel state information, which leads to a combinatorial problem to optimize the sum rate. The exact solution to this problem requires an exhaustive search; however, the total number of candidates to search through can be extremely large in massive MIMO systems. To address this challenge, we propose three sub-optimal algorithms. The first is a tabu search-based algorithm, which examines the neighbors of the current solution to antenna-to-sub-array allocation and moves to the best one in each iteration. The second is a sequential antenna-reallocation algorithm with reduced complexity, which updates an antenna-to-sub-array assignment in each iteration. The third algorithm, that is, greedy antenna allocation, is composed of two sub-optimal algorithms. In its first part, only a single antenna is allocated to each sub-array based on a conventional algorithm. Then, in the second part, the antennas are added sequentially to each sub-array to optimize the system sum rate. The numerical results demonstrate that the DUESA architecture achieves substantially higher total achievable rates than conventional PC-HBF schemes. Furthermore, it has approximately 10%–20% higher energy efficiency than the conventional UESA architecture. [ABSTRACT FROM AUTHOR]

In the conventional tabu search (TS) detection algorithm for multiple-input multiple-output (MIMO) systems, the metrics of all neighboring vectors are computed to determine the best one to move to. This strategy requires high computational complexity, especially in large MIMO systems with high-order modulation schemes such as 16- and 64-QAM signaling. This paper proposes a novel reduced-complexity TS detection algorithm called neighbor-grouped TS (NG-TS), which divides the neighbors into groups and finds the best neighbor by using a simplified cost function. Furthermore, based on the complexity analysis of NG-TS, we propose a channel ordering scheme that further reduces its complexity. Simulation results show that the proposed NG-TS with channel ordering can achieve up to 85% complexity reduction with respect to the conventional TS algorithm with no performance loss in both low- and higher-order modulation schemes. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive threshold-aided K-best sphere decoding (AKSD) algorithm for large multiple-input multiple-output systems. In the proposed scheme, to reduce the average number of visited nodes compared to the conventional K-best sphere decoding (KSD), the threshold for retaining the nodes is adaptively determined at each layer of the tree. Specifically, we calculate the adaptive threshold based on the signal-to-noise ratio and index of the layer. The ratio between the first and second smallest accumulated path metrics at each layer is also exploited to determine the threshold value. In each layer, in addition to the K paths associated with the smallest path metrics, we also retain the paths whose path metrics are within the threshold from the Kth smallest path metric. The simulation results show that the proposed AKSD provides nearly the same bit error rate performance as the conventional KSD scheme while achieving a significant reduction in the average number of visited nodes, especially at high signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

In the conventional tabu search (TS) detection algorithm for multiple-input multiple-output (MIMO) systems, the cost metrics of all neighboring vectors are computed to determine the best neighbor. This can require an excessively high computational complexity, especially in large MIMO systems because the number of neighboring vectors and the dimension per vector are large. In this study, we propose an improved TS algorithm based on the QR decomposition of the channel matrix (QR-TS), which allows for finding the best neighbor with a significantly lower complexity compared with the conventional TS algorithm. Specifically, QR-TS does not compute all metrics by early rejecting unpromising neighbors, which reduces the computational load of TS without causing any performance loss. To further optimize the QR-TS algorithm, we investigate novel ordering schemes, namely the transmit-ordering (Tx-ordering) and receive-ordering (Rx-ordering), which can considerably reduce the complexity of QR-TS. Simulation results show that QR-TS reduces the complexity approximately by a factor of two compared with the conventional TS. Furthermore, when both Tx-ordering and Rx-ordering are applied, QR-TS requires approximately $60\%\text{ -- }90\%$ less complexity compared with the conventional TS scheme. The proposed algorithms are suitable for both low-order and high-order modulation, and can achieve a significant complexity reduction compared to the Schnorr–Euchner and $K\text{-}$ best sphere decoders in large MIMO systems. [ABSTRACT FROM AUTHOR]

In this study, low-complexity sphere decoding (SD) algorithms for generalized spatial modulation (GSM) are proposed. To reduce the computational complexity of the SD algorithm for GSM, layer ordering and transmit antenna combination (TAC) partitioning are employed. The layer ordering scheme determines the detection order of layers based on their reliabilities, which are measured by their minimum mean-square errors (MMSEs). In TAC partitioning, which is combined with the early termination technique, the likely TACs are selected by exploiting an MMSE-based weighting metric to reduce the search space. Simulation results demonstrate that the proposed ordering and partitioning aided SD schemes achieve nearly the same performance as the conventional SD scheme; however, they require significantly low complexity, especially in large GSM multiple-input-multiple-output systems and in the low SNR region. [ABSTRACT FROM AUTHOR]

This correspondence paper proposes a low-complexity adaptive spatial modulation (ASM) scheme, which is based on sphere search (SS). To find the optimal ASM mode, the existing ASM schemes examine each error vector in the search space. By contrast, in the proposed SS-aided strategy, the search space is reduced to the error vectors inside a sphere, which results in significant complexity reduction. By exploiting a similar structure between the error vectors in ASM and symbol vectors in generalized spatial modulation (GSM), we modify the sphere decoder for GSM to develop an efficient two-step SS scheme for ASM. In addition, an early termination scheme is employed to further reduce the computational load of the SS process for each ASM mode. Simulation results show that SS-aided ASM provides a significant reduction in computational complexity compared to the existing ASM schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a linear interference alignment (IA) scheme, which can be used for uplink channels in a general multicell multiuser multiple-input-multiple-output (MIMO) cellular network. The proposed scheme aims to align interference caused by signals from a set of transmitters into a subspace that is established by the signals from only a subset of those transmitters, thereby effectively reducing the number of interfering transmitters. The total degrees of freedom (DoF) achievable by the proposed scheme is given in closed-form expression, and a numerical analysis shows that the proposed scheme can achieve the optimal DoF in certain scenarios and provides a higher total DoF than other related schemes in most cases. [ABSTRACT FROM AUTHOR]

In this paper, we present uplink interference alignment (IA) schemes for two-cell multiple-input–multiple-output (MIMO) channels. Assuming that each cell has K interfering users, we propose an IA scheme for the full interference channel (FIC) and derive the achievable degrees of freedom (DOFs). The proposed scheme is extended to a two-cell Z-interference channel (ZIC), where only one of the two cells is subject to interference. The achievable DOF for the two-cell ZIC channel is also derived, and it is compared with that of the FIC with the IA and that of the conventional orthogonal scheme based on numerical results. [ABSTRACT FROM PUBLISHER]