Massive Multiple-input Multiple-output Beamforming Research Articles

Distributed Learning for Uplink Cell-Free Massive MIMO Networks

Cell-free massive multiple-input multiple-output (MIMO) can resolve the inter-cell interference issue in cellular networks through cooperative beamforming of the distributed access points (APs). This paper focuses on an uplink cell-free massive MIMO network and investigates novel methods to train the central processing unit (CPU), the APs, and the users in the network. To reduce the communication burden posed on the fronthaul, each AP applies receive beamforming to compress the vector signals into scalar ones before passing them to the CPU for centralized processing. By drawing analogies between an uplink cell-free network and a quasi-neural network and borrowing the idea of backpropagation algorithm, we propose a novel scheme named the distributed learning for uplink cell-free massive MIMO beamforming (DLCB), which can achieve the multi-AP cooperation without explicit estimation of their channel state information (CSI). The DLCB has low computational complexity and is applicable to various objective functions, such as the minimum mean squared error criterion and the maximum sum rate criterion. Extensive simulations verify that the proposed scheme achieves superior performance over the state-of-the-art methods.

Dynamic mmWave Channel Emulation in a Cost-Effective MPAC With Dominant-Cluster Concept

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) has been considered as a key enabler for the fifth-generation (5G) communications. It is essential to design and test mmWave 5G devices under various realistic scenarios since the radio propagation channels pose intrinsic limitations on the performance. This requires emulating realistic dynamic mmWave channels in a reproducible manner in laboratories, which is the goal of this article. In this contribution, we first illustrate the dominant-cluster(s) concept, where the nondominant clusters in the mmWave channels are pruned, for mmWave 5G devices applying massive MIMO beamforming. This demonstrates the importance and necessity to accurately emulate the mmWave channels at a cluster level rather than the composite-channel level. Thus, an over-the-air (OTA) emulation strategy for dynamic mmWave channels is proposed based on the concept of the dominant cluster(s) in a sectored multiprobe anechoic chamber (SMPAC). The key design parameters, including the probe number and the angular spacing of probes, are investigated through comprehensive simulations. A cost-effective switch circuit is also designed for this purpose and validated in the simulation. Furthermore, a dynamic mmWave channel measured in an indoor scenario at 28–30 GHz is presented, where the proposed emulation strategy is also validated by reproducing the measured reality.

Massive MIMO Beamforming in Monostatic Backscatter Multi-Tag Networks

This letter investigates the role of massive multiple-input multiple-output (MIMO) in improving the spectral and energy efficiency of monostatic backscatter communication systems, where a multiple-antenna reader aims to decode information backscattered from multiple tags. Specifically, we investigate the performance of the two most prominent precoders and combiners, namely, the matched filter and zero forcing. First, we derive capacity lower bounds for the four different underlying transceiver design configurations. Then, asymptotic analysis is conducted and it is shown that with perfect channel state information, the total transmit power can be scaled down by a factor of the square of the number of transmit antennas without loss in the performance. To further corroborate the practical utility of the considered massive MIMO multi-tag setting, the optimization of the backscatter coefficients for sum rate maximization and the effect of imperfect channel state information are also considered.

A comprehensive overview of recent developments in RF-MEMS technology-based high-performance passive components for applications in the 5G and future telecommunications scenarios

The goal of this work is to provide an overview about the current development of radio-frequency microelectromechanical systems technology, with special attention towards those passive components bearing significant application potential in the currently developing 5G paradigm. Due to the required capabilities of such communication standard in terms of high data rates, extended allocated spectrum, use of massive MIMO (Multiple- Input-Multiple-Output) systems, beam steering and beam forming, the focus will be on devices like switches, phase shifters, attenuators, filters, and their packaging/integration. For each of the previous topics, several valuable contributions appeared in the last decade, underlining the improvements produced in the state of the art and the chance for RFMEMS technology to play a prominent role in the actual implementation of the 5G infrastructure.

A Correlation Theory of Antenna Directivity With Applications to Superdirective Arrays

We develop a general approach to antenna directivity by combining the infinitesimal dipole model (IDM) and the cross-correlation Green's function (CGF) in a new formulation. The theory is deployed for the analysis and synthesis of antenna systems via the proposed IDM-CGF method after integration with global optimization algorithms. The IDM-CGF theory opens the door for hitherto unexplored connections between applications involving antenna gain engineering on one side, and multiple-input–multiple-output (MIMO) and diversity methods on the other, helping to devise new techniques for applications like massive MIMO and millimeter-wave beamforming algorithms, wireless power transfer, in-package antennas, and full three-dimensional isotropicity.

Hybrid Beamforming for Millimeter-Wave Heterogeneous Networks

Heterogeneous networks (HetNets) employing massive multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) technologies have emerged as a promising solution to enhance the network capacity and coverage of next-generation 5G cellular networks. However, the use of traditional fully-digital MIMO beamforming methods, which require one radio frequency (RF) chain per antenna element, is not practical for large-scale antenna arrays, due to the high cost and high power consumption. To reduce the number of RF chains, hybrid analog and digital beamforming has been proposed as an alternative structure. In this paper, therefore, we consider a HetNet formed with one macro-cell base station (MBS) and multiple small-cell base stations (SBSs) equipped with large-scale antenna arrays that employ hybrid analog and digital beamforming. The analog beamforming weight vectors of the MBS and the SBSs correspond to the the best-fixed multi-beams obtained by eigendecomposition schemes. On the other hand, digital beamforming weights are optimized to maximize the receive signal-to-interference-plus-noise ratio (SINR) of the effective channels consisting of the cascade of the analog beamforming weights and the actual channel. The performance is evaluated in terms of the beampatterns and the ergodic channel capacity and shows that the proposed hybrid beamforming scheme achieves near-optimal performance with only four RF chains while requiring considerably less computational complexity.

A Joint Scheduling and Beamforming Scheme for RoF-Aided MC-SSN

Applied to harsh scenarios, the mission critical sensors and sensor network (MC-SSN) is expected to be of high capacity, density, and low cost. Both the industry and research community have a consensus on the need for energy and spectrum saving. Radio over fiber (RoF) system is promising for low latency and flexible system of high efficiency. In this paper, we consider the RoF-aided MC-SSN system serving the sensors with massive multiple-input multiple-output (MIMO) equipped. Based on this model, the base stations, relays, and other network elements can set up connections with each other via optical fiber or wireless links. Aiming to maximize the energy efficiency, this paper proposes a scheme including two aspects: 1) scheduling of the sensors based on the direction of arrival sensing of interference an 2) massive MIMO beamforming matrices generation based on a codebook. The numerical results show that the proposed scheduling and resource allocation method can achieve sufficient throughput with reduced power consumption for the RoF-aided MC-SSN system.

Delay-aware packet scheduling for massive MIMO beamforming transmission using large-scale reinforcement learning

This work addresses the massive multiple input multiple output (MIMO) beamforming scheduling problem. The scheduling model for packet transmission with paralleled caches using massive MIMO multicast beamforming is proposed by using reinforcement learning (RL) theory to denote the mapping function from the system state to the beamforming strategy. The delay-aware model-free RL scheduling problem for massive MIMO multicast beamforming is derived and analyzed in asymptotic condition. We show that the model-free RL method can be a complementary way to optimize the packet delay when the channel state information (CSI) is unavailable and the traditional convex optimization based methods are consequently ineffective. To derive low-complexity algorithm, we build the RL sub-problems for each specific multicast group which are asymptotically independent. The policy gradient method is used to solve the proposed RL problems. In numerical experiments we provide simulation results under different number of transmitting antennas and provide performance comparison between the proposed method and randomized method. It shows that even without using CSI, the RL based policy can still dynamically optimize the delay of the system and out-perform the randomized policy.

Frequency Reuse of Beam Allocation for Multiuser Massive MIMO Systems

Massive multiple-input-multiple-output (MIMO) has become a promising technique to provide high-data-rate communication in fifth-generation mobile systems, thanks to its ability to form narrow and high-gain beams. Among various massive MIMO beamforming techniques, the fixed-beam scheme has attracted considerable attention due to its simplicity. In this paper, we focus on a fixed-beam based multiuser massive MIMO system, where each user is served by a beam allocated to it. To maximize the sum data rate, a greedy beam allocation algorithm is proposed under the practical condition that the number of radio frequency chains is smaller than the number of users. Simulation results show that our proposed greedy algorithm achieves nearly optimal sum data rate. As only the sum data rate is optimized, there are some “worst-case” users, who could suffer from strong inter-beam interference and thus experience low data rate. To improve the individual data rates of the worst-case users while maintaining the sum data rate, an adaptive frequency reuse scheme is proposed. Simulation results corroborate that our proposed adaptive frequency reuse strategy can greatly improve the worst-case users’ data rates and the max-min fairness among served users without sacrificing the sum data rate.

A 16 × 8 Hybrid Beamforming and Precoding Processor for 2D Planar Antenna Array in mmWave MIMO Systems

In the next generation 5G mobile communication systems, the massive multiple-input multiple-output (MIMO) system uses large antenna arrays to improve transmission throughput and reliability. Millimeter wave (mmWave) is very suitable for massive MIMO systems because its small wavelength allows the tightly package of large antenna array. This paper presents a low-complexity hybrid MIMO beamforming and precoding processor for 16x8 2D planar antenna array in mmWave systems. This study proposes to use the antenna selection technique to reduce the MIMO dimension and presents a sliding-window-based index selection for RF beamforming array vector. These techniques not only reduce the computational complexity but also enable highly paralleled hardware architecture. The proposed algorithm was designed and implemented by using TSMC 90 nm CMOS Technology for the MIMO baseband processing in 16 ×8 2D planar antenna array MIMO systems. The processor achieves 2.9 M, 3.3 M, 4 M, and 4.7 M channel matrices per second for four, three, two, and one stream, respectively.

Massive MIMO Beamforming With Transmit Diversity for High Mobility Wireless Communications

Providing stable and fast data transmission service is challenging in a high mobility wireless communication system, where massive multiple-input multiple-output (MIMO) beamforming is deemed as a potential solution. In the literature, the majority of previous works focused on how to optimize the beamforming scheme with traditional side information like perfect or imperfect channel state information (CSI) in non-mobile or low mobility scenarios. However, it is hard to either track the channel or obtain perfect CSI in the high mobility scenario without large online computation, because the wireless channel appears to be fast time varying and double-selective in the spatial-temporal domain. In this paper, by exploiting the special characters in the high mobility scenario, we introduce an applicable low-complexity beamforming scheme with transmit diversity in the high mobility scenario with the aid of location information. The beam is generated and selected mainly based on the location information, where the beam weight is optimized to maximize the total service that one BS can provide. Moreover, to guarantee a full diversity gain in this joint scheme, an optimal beam selection algorithm is proposed. Besides, to maximize the total service competence of one base station, a closed-form power allocation solution for the multi-user scenario is derived. To solve the potential inter-beam interference in massive MIMO system, a location-aided algorithm is proposed to eliminate the interference and maximize the mobile service of the whole train at the same time. Theoretical analysis and multiple simulation results show that our scheme approaches the theoretical performance bound of adaptive beamforming scheme but with much lower complexity.

Teaching the Principles of Massive MIMO: Exploring reciprocity-based multiuser MIMO beamforming using acoustic waves

Massive multiple-input, multiple-output (MIMO) is currently the most compelling wireless physical layer technology and a key component of fifth-generation (5G) systems. The understanding of its core principles has emerged during the last five years, and material is becoming available that is rigorously refined to focus on timeless fundamentals [1], facilitating the instruction of the topic to both master- and doctoral-level students [2]. Meaningful laboratory work that exposes the operational principles of massive MIMO is more difficult to accomplish. At Linkoping University, Sweden, this was achieved through a project course, based on the conceive-design-implement-operate (CDIO) concept [3], and through the creation of a specially designed experimental setup using acoustic signals.

Performance Evaluation of Analog Beamforming with Hardware Impairments for mmW Massive MIMO Communication in an Urban Scenario

The use of massive multiple-input multiple-output (MIMO) techniques for communication at millimeter-Wave (mmW) frequency bands has become a key enabler to meet the data rate demands of the upcoming fifth generation (5G) cellular systems. In particular, analog and hybrid beamforming solutions are receiving increasing attention as less expensive and more power efficient alternatives to fully digital precoding schemes. Despite their proven good performance in simple setups, their suitability for realistic cellular systems with many interfering base stations and users is still unclear. Furthermore, the performance of massive MIMO beamforming and precoding methods are in practice also affected by practical limitations and hardware constraints. In this sense, this paper assesses the performance of digital precoding and analog beamforming in an urban cellular system with an accurate mmW channel model under both ideal and realistic assumptions. The results show that analog beamforming can reach the performance of fully digital maximum ratio transmission under line of sight conditions and with a sufficient number of parallel radio-frequency (RF) chains, especially when the practical limitations of outdated channel information and per antenna power constraints are considered. This work also shows the impact of the phase shifter errors and combiner losses introduced by real phase shifter and combiner implementations over analog beamforming, where the former ones have minor impact on the performance, while the latter ones determine the optimum number of RF chains to be used in practice.