Massive Massive Multiple-input Multiple-output Networks Research Articles

Efficient Adaptive Subarrays in Millimeter-Wave MIMO Systems With Hybrid RF/Baseband Precoding/Combining Design

This paper concentrates on the computationally efficient sub-connected mapping strategies from radio frequency (RF) chains to antennas in millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems with hybrid RF/baseband precoding/combining design. Such an adaptive subarray architecture offers a tradeoff between hardware complexity and spectral efficiency in massive MIMO networks at mmWave frequencies. Three proposed low-complexity dynamically connected structures comprise the Manhattan metric (MM) with/without overlapping, contiguous subarray correlation (CSC), and equally neighboring correlation design techniques. On the basis of the achievable sum-rate (ASR) maximization, the proposed adaptive RF chain-antenna planning methodologies are used to reconfigure effectively RF-chain associated subarrays. Remarkably, both the nonoverlapped MM and the CSC dynamically connected configurations possess excellent advantages of ASR performance and computational complexity simultaneously at a fixed hardware requirement in terms of phase shifters. Finally, simulation results demonstrate that the proposed adaptive partially connected networks are capable of accomplishing an ASR capacity approaching the fully digital and fully connected design with an alleviated demand on both computation and hardware complexities. Furthermore, with the same amount of phase shifters, all the proposed dynamically constructed subarrays outperform the partially connected but fixed architectures.

Pilot Decontamination Based on Superimposed Pilots Assisted by Time-Multiplexed Pilots in Massive MIMO Networks

Pilot contamination has been recognized as one of the main limiting factors in massive multiple-input multiple-output (MIMO) since it damages the acquisition of accurate channel state information (CSI) at base stations. In this work, we propose a novel pilot decontamination approach in a massive MIMO network where the time division duplex operation is assumed. In our proposed approach, user equipments are allowed to use superimposed (SP) pilots in combination with time-multiplexed (TM) pilots to estimate the CSI prior to downlink data transmission. Specifically, the contaminated channel estimates obtained by TM pilots are used to reduce the amount of interference caused by transmitting pilots together with uplink data, which is a typical problem in SP pilots-based channel estimation. Through numerical results, we show that the interference that emerges from transmitting pilots with data is substantially reduced by our proposed approach. This reduction, in combination with the reduced overhead in TM pilots transmission, leads to an improved spectral efficiency achieved in massive MIMO networks.

Large-System Mutual Information Analysis of Receive Spatial Modulation in Correlated Multi-Cell Massive MIMO Networks

In this paper, the receive spatial modulation (RSM) scheme is studied in the downlink of the multi-cell multi-user systems, assuming that the base station (BS) is equipped with a large number of antennas and serves a large number of multi-antenna users, while the user-antenna ratio is bounded. The system model is practical and applicable in a comprehensive manner, accounting for channel estimation, spatially correlated channels, pilot contamination, and path loss. Combining the concepts of RSM and massive multiple-input multiple-output (MIMO), the spectral efficiency of linear preprocessing zero-forcing (ZF) and regularized ZF (RZF) methods is studied by approximating the probability of antenna detection and deriving a lower bound for the spatial-domain mutual information. With the aid of the large-system analysis, the deterministic equivalent expressions are provided for the achievable rates that are asymptotically exact. The Monte Carlo simulations validate this claim and show that for limited dimensions, the proposed deterministic equivalents yield accurate approximations, even for strongly correlated channels. The results further show that in comparison with the conventional massive MIMO systems with single-antenna users, equipping each user with multiple antennas and applying the RSM scheme enhances the spectral efficiency when each cell is not heavily loaded.

Hybrid Analog-Digital Beamforming Design for SE and EE Maximization in Massive MIMO Networks

Hybrid analog-digital (HAD) beamforming architectures have been proposed to facilitate the practical implementation of massive multiple-input multiple-output (MIMO) systems by reducing the number of employed radio frequency chains. While most prior studies have aimed to maximize spectral efficiency (SE), the present paper proposes a two-stage HAD beamforming design for multi-user MIMO systems that can be used to maximize either the system's overall energy efficiency (EE) or SE. This problem is nonconvex and NP-hard due to the joint optimization between the analog and digital domains and the constant modulus constraints required by the analog domain. To address this problem, we propose a decoupled two-stage design wherein the first stage, the analog beamforming parts are updated, which are then taken into account in the second stage to design the digital beamforming parts to maximize the system's EE or SE. We consider two widely-used HAD beamforming techniques that utilize either fully-connected (FC) or partially-connected (PC) architectures employing variable phase-shifters. Using the most recently available data for the circuitry power consumption of the components, we compare the performance of these two HAD architectures with that of the fully-digital (FD) architecture in terms of the total circuitry power consumption, and achieved SE and EE. We find that there is a certain number of users above which the FC architecture has higher circuitry power consumption than the FD counterpart, in contrast to the PC architecture that always has lower circuitry power consumption. More importantly, our results reveal, contrary to the common opinion, that depending on the circuitry parameters the FD architecture may achieve not only higher SE, but also higher EE than the HAD architectures.

Price-Based Resource Allocation in Wireless Power Transfer-Enabled Massive MIMO Networks.

This paper considers the price-based resource allocation problem for wireless power transfer (WPT)-enabled massive multiple-input multiple-output (MIMO) networks. The power beacon (PB) can transmit energy to the sensor nodes (SNs) by pricing their harvested energy. Then, the SNs transmit their data to the base station (BS) with large scale antennas by the harvesting energy. The interaction between PB and SNs is modeled as a Stackelberg game. The revenue maximization problem of the PB is transformed into the non-convex optimization problem of the transmit power and the harvesting time of the PB by backward induction. Based on the equivalent convex optimization problem, an optimal resource allocation algorithm is proposed to find the optimal price, energy harvesting time, and power allocation for the PB to maximize its revenue. Finally, simulation results show the effectiveness of the proposed algorithm.

Secure Communications Over Cell-Free Massive MIMO Networks With Hardware Impairments

This paper investigates the effect of hardware impairments on the physical layer security for a cell-free massive multiple-input multiple-output (MIMO) network in the presence of pilot spoofing attack. By employing a classical additive hardware distortion model, the joint hardware impairment effects brought by both access points (APs) and user equipments have been taken into account, whereas the eavesdropper is assumed to be equipped with perfect hardware. Thereby, we derive a closed-form lower bound for the ergodic secrecy rate in the presence of imperfect channel state information. To obtain further insights, we investigate asymptotic secrecy performance under different hardware scaling factors. It proves that the hardware-quality scaling effect vanishes as the number of APs increases in the considered secure cell-free massive MIMO system with active attack. Furthermore, by using continuous approximation and path-following algorithms, the optimal power allocation scheme is obtained to maximize the achievable secrecy rate. Numerical results validate the derived results and the efficiency of the proposed power allocation scheme.

Optimized Power Control for Massive MIMO With Underlaid D2D Communications

In this paper, we consider device-to-device (D2D) communication that is underlaid in a multi-cell massive multiple-input multiple-output (MIMO) system and propose a new framework for power control and pilot allocation. In this scheme, the cellular users (CUs) in each cell get orthogonal pilots which are reused with reuse factor one across cells, while all the D2D pairs share another set of orthogonal pilots. We derive a closed-form capacity lower bound for the CUs with different receive processing schemes. In addition, we derive a capacity lower bound for the D2D receivers and a closed-form approximation of it. We provide power control algorithms to maximize the minimum spectral efficiency (SE) and maximize the product of the signal-to-interference-plus-noise ratios in the network. Different from prior works, in our proposed power control schemes, we consider joint pilot and data transmission optimization. Finally, we provide a numerical evaluation where we compare our proposed power control schemes with the maximum transmit power case and the case of conventional multi-cell massive MIMO without D2D communication. Based on the provided results, we conclude that our proposed scheme increases the sum SE of multi-cell massive MIMO networks.

Rate Adaptation for Downlink Massive MIMO Networks and Underlaid D2D Links: A Learning Approach

In this paper, a novel learning-based rate adaptation mechanism is proposed for a downlink massive multiple-input-multiple-output (MIMO) network with underlaid device-to-device (D2D) links, where the link signal-to-interference-plus-noise ratio (SINR) cannot be accurately predicted before transmission, even its distribution statistics are unknown at the very beginning. Specifically, two coexistence schemes are considered: (1) the D2D links only reuse the downlink subframes; and (2) the D2D receivers also join the uplink channel estimation of the associated cells. For the second scheme, the downlink interference to the D2D receivers is suppressed at the cost of channel training overhead. The geographic distributions of the selected downlink and D2D users in each frame are modeled as two independent stochastic processes with unknown statistics. As a result, the distribution of interference power is unknown to the transmitters. In order to facilitate robust rate allocation, we first derive the asymptotic expressions of downlink and D2D signal-to-interference-plus-noise ratios (SINRs) for sufficiently large antenna number, and show that their distributions can be approximated by Gaussian or exponential random variables. Subsequently, distributive learning algorithms are proposed to evaluate the means and variances of these random variables. This enables the BSs and D2D transmitters to determine the transmission rates under a constraint on packet outage probability.

Separate analysis of cell‐edge and cell‐centre user performance for irregular massive MIMO network with interference cancellation

How much performance gain for the user equipment (UE) located at cell-edge can be obtained, from the interference cancellation (IC) by exploiting extra degrees of freedom in multi-user massive multiple-input multiple-output (MIMO), needs to be investigated. Also, it is necessary to reveal the impact of this IC on the performance of cell-centre UE (CCU). In this study, the authors separately analyse the coverage probability of cell-edge UE (CEU) and CCU under the IC in irregular network utilising tools from stochastic geometry. Specifically, the closed-form expressions for the upper bound of CEU coverage probability and the lower bound of coverage probability for CCU are given out. For large threshold scenario, the authors show the potential of massive MIMO with IC on achieving the cell-centre-like experience in a more simple closed-form formula. Analytical and numerical results show that a reasonable balance between the CEU performance enhancement and the CCU performance degradation under the IC can be reached. Moreover, slight influence of cell-edge area on the coverage probability for both CEU and CCU is observed. Finally, it is found that the coverage probability of CEU is more sensitive to the variation of density of UEs and the base stations than that of CCU.

Wireless Power Transfer in mmWave Massive MIMO Systems With/Without Rain Attenuation

This paper studies the performance of wireless power transfer (WPT) in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems operating in rainy or non-rainy conditions. Accounting for rainfall effects, path loss, and small-scale fading, a comprehensive channel model suitable for modeling the energy propagation in mmWave massive MIMO systems is first developed. Based on this model, a framework for the channel estimation necessary at the hybrid data-and-energy access point (HAP) is provided, and various analytical results on the estimated channel matrix are obtained. Then, using the law of energy conservation, the downlink energy transferred by the HAP and harvested by the user equipments (UEs) is analyzed, and investigated in several important scenarios. The results reveal that the asymptotic harvested energy increases linearly with the number of HAP antennas and the number of UEs, whereas it decreases exponentially with the rain parameters which monotonically increase with the operating frequency. It is also demonstrated that severe rain attenuation can even make the WPT impossible. Afterwards, the scenario where UEs are randomly distributed is investigated, and important insights are gained. In particular, for a WPT system with coverage radius $R_{\mathrm {n}}$ and exclusion radius $R_{\mathrm {e}}$ , the average asymptotic harvested energy significantly increases with decreasing $R_{\mathrm {e}}$ and $R_{\mathrm {n}}$ , which confirms that small-cells configurations will be viable solutions for enhancing WPT performances in mmWave massive MIMO networks.

Performance Analysis of Multi-Cell Millimeter-Wave Massive MIMO Networks With Low-Precision ADCs

In this paper, we investigate a multi-cell millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) network with low-precision analog-to-digital converters (ADCs) at the base station (BS). Each cell serves multiple users and each user is equipped with multiple antennas but driven by a single RF chain. We first introduce a channel estimation strategy for the mmWave massive MIMO network and analyze the achievable rate with imperfect channel state information. Then, we derive an insightful lower bound for the achievable rate, which becomes tight with a growing number of users. The bound clearly demonstrates the impacts of the number of antennas and the ADC precision, especially for a single-cell mmWave network at low signal-to-noise ratio (SNR). It characterizes the tradeoff among various system parameters. Our analytical results are finally confirmed by extensive computer simulations.

Random Access in Massive MIMO by Exploiting Timing Offsets and Excess Antennas

Massive multiple-input multiple-output (MIMO) systems, where base stations (BSs) are equipped with hundreds of antennas, are an attractive way to handle the rapid growth of data traffic. As the number of user equipments (UEs) increases, the initial access and handover in contemporary networks will be flooded by user collisions. In this paper, a random access protocol is proposed that resolves collisions and performs timing estimation by simply utilizing the large number of antennas envisioned in massive MIMO networks. UEs entering the network perform spreading in both time and frequency domains, and their timing offsets are estimated at the BS in closed form using a subspace decomposition approach. This information is used to compute channel estimates that are subsequently employed by the BS to communicate with the detected UEs. The favorable propagation conditions of massive MIMO suppress interference among UEs, whereas the inherent timing misalignments improve the detection capabilities of the protocol. Numerical results are used to validate the performance of the proposed procedure in massive MIMO networks, under uncorrelated and correlated fading channels. With $2.5 \times 10^{3}$ UEs that may simultaneously become active with probability 1%, a total of 16 frequency–time codes, and 100 antennas, a given UE is detected with probability 75%, and the accuracy of its timing estimate is on the order of few samples.

Asymptotic Task-Based Quantization With Application to Massive MIMO

Quantizers take part in nearly every digital signal processing system which operates on physical signals. They are commonly designed to accurately represent the underlying signal, regardless of the specific task to be performed on the quantized data. In systems working with high-dimensional signals, such as massive multiple-input multiple-output (MIMO) systems, it is beneficial to utilize low-resolution quantizers, due to cost, power, and memory constraints. In this work we study quantization of high-dimensional inputs, aiming at improving performance under resolution constraints by accounting for the system task in the quantizers design. We focus on the task of recovering a desired signal statistically related to the high-dimensional input, and analyze two quantization approaches: We first consider vector quantization, which is typically computationally infeasible, and characterize the optimal performance achievable with this approach. Next, we focus on practical systems which utilize hardware-limited scalar uniform analog-to-digital converters (ADCs), and design a task-based quantizer under this model. The resulting system accounts for the task by linearly combining the observed signal into a lower dimension prior to quantization. We then apply our proposed technique to channel estimation in massive MIMO networks. Our results demonstrate that a system utilizing low-resolution scalar ADCs can approach the optimal channel estimation performance by properly accounting for the task in the system design.

Robust user scheduling with COST 2100 channel model for massive MIMO networks

The problem of user scheduling with reduced overhead of channel estimation in the uplink of massive multiple-input multiple-output (MIMO) systems has been investigated. The authors consider the COST 2100 channel model. In this paper, they first propose a new user selection algorithm based on knowledge of the geometry of the service area and location of clusters, without having full channel state information at the BS. They then show that the correlation in geometry-based stochastic channel models (GSCMs) arises from the common clusters in the area. In addition, exploiting the closed-form Cramer–Rao lower bounds, the analysis for the robustness of the proposed scheme to cluster position errors is presented. It is shown by analysing the capacity upper bound that the capacity strongly depends on the position of clusters in the GSCMs and users in the system. Simulation results show that though the BS receiver does not require the channel information of all users, by the proposed geometry-based user scheduling algorithm the sum rate of the system is only slightly less than the well known greedy weight clique scheme.

Channel Hardening and Favorable Propagation in Cell-Free Massive MIMO With Stochastic Geometry

Cell-free (CF) massive multiple-input multiple-output (MIMO) is an alternative topology for future wireless networks, where a large number of single-antenna access points (APs) are distributed over the coverage area. There are no cells but all users are jointly served by the APs using network MIMO methods. Prior works have claimed that the CF massive MIMO inherits the basic properties of cellular massive MIMO, namely, channel hardening and favorable propagation. In this paper, we evaluate if one can rely on these properties when having a realistic stochastic AP deployment. Our results show that channel hardening only appears in special cases, for example, when the pathloss exponent is small. However, by using 5–10 antennas per AP, instead of one, we can substantially improve the hardening. Only spatially well-separated users will exhibit favorable propagation, but when adding more antennas and/or reducing the pathloss exponent, it becomes more likely for favorable propagation to occur. The conclusion is that we cannot rely on the channel hardening and the favorable propagation when analyzing and designing the CF massive MIMO networks, but we need to use achievable rate expressions and resource allocation schemes that work well also in the absence of these properties. Some options are reviewed in this paper.

Base Station Selection for Massive MIMO Networks With Two-Stage Precoding

The two-stage precoding has been proposed to reduce the overhead of both the channel training and the channel state information (CSI) feedback for the massive multiple-input multiple-output (MIMO) system. But the overlap of the angle-spreading-ranges (ASR) for different user clusters may seriously degrade the performance of the two-stage precoding. In this letter, we propose one ASR overlap mitigating scheme through the base station (BS) selection. Firstly, the BS selection is formulated as a sum signal-to-interference-plus-noise ratio (SINR) maximization problem. Then, the problem is solved by a low-complex algorithm through maximizing signal-to-leakage-plus-noise ratio (SLNR). In addition, we propose one low-overhead algorithm with the lower bound on the average SLNR as the objective function. Finally, we demonstrate the efficacy of the proposed schemes through the numerical simulations.

Massive MIMO Wireless Networks: An Overview

Massive multiple-input-multiple-output (MIMO) systems use few hundred antennas to simultaneously serve large number of wireless broadband terminals. It has been incorporated into standards like long term evolution (LTE) and IEEE802.11 (Wi-Fi). Basically, the more the antennas, the better shall be the performance. Massive MIMO systems envision accurate beamforming and decoding with simpler and possibly linear algorithms. However, efficient signal processing techniques have to be used at both ends to overcome the signaling overhead complexity. There are few fundamental issues about massive MIMO networks that need to be better understood before their successful deployment. In this paper, we present a detailed review of massive MIMO homogeneous, and heterogeneous systems, highlighting key system components, pros, cons, and research directions. In addition, we emphasize the advantage of employing millimeter wave (mmWave) frequency in the beamforming, and precoding operations in single, and multi-tier massive MIMO systems.

NOMA Meets Finite Resolution Analog Beamforming in Massive MIMO and Millimeter-Wave Networks

Finite resolution analog beamforming (FRAB) has been recognized as an effective approach to reduce hardware costs in massive multiple-input multiple-output (MIMO) and millimeter-wave networks. However, the use of FRAB means that the beamformers are not perfectly aligned with the users' channels and multiple users may be assigned similar or even identifical beamformers. This letter shows how non-orthogonal multiple access (NOMA) can be used to exploit this feature of FRAB, where a single FRAB based beamformer is shared by multiple users. Both analytical and simulation results are provided to demonstrate the excellent performance achieved by this new NOMA transmission scheme.

Precoding and power allocation algorithms for device-to-device communication in massive MIMO networks

Device-to-device communication (D2D) and massive multiple input multiple output (MIMO) systems are two emerging technologies that are being considered to improve the performance of next generation wireless cellular systems. In D2D, two mobile nodes communicate directly without traversing the base station (BS). Consequently, interference management, coordination, and/or cancellation techniques have to be adopted to target the problem of mutual interference between the D2D devices and the BS (or the normal mobiles nodes connected to the BS). In this paper, we investigate the problem of BS precoder design and D2D devices power allocation in the downlink of a single-cell network assuming existence of D2D devices as well as massive MIMO at the BS. We propose algorithms to maximize the sum of the achievable data rates of the D2D pairs while maintaining quality of service constraints on the cellular user equipment, which communicate normally with the BS. We also propose two algorithms for the precoding problem; the first is based on semi-definite programming while the second is based on gradient descent algorithms. Moreover, we investigate two solutions for the power allocation problem; the first solves an approximate convex optimization problem iteratively while the second is a suboptimal, but far less complex, heuristic. Finally, we propose a technique to apply the mentioned solutions when only partial channel state information is available at the BS. Simulation results show that the proposed solutions are superior to the conventional precoding and power allocation schemes.

Uplink Pilot Design for Multi-Cell Massive MIMO Networks

In massive multiple-input multiple-output (MIMO) systems, the effect of inter-user interference and noise can be suppressed through simple signal processing techniques. However, pilot contamination, which results from the use of a correlated pilot, causes performance bottleneck for massive MIMO networks. In this letter, a pilot design algorithm based on alternating minimization is developed to alleviate the pilot contamination in multi-cell massive MIMO systems. Contrary to the existing pilot reuse protocols that restrict the pilot length to be an integer multiple of the number of users, the proposed method designs a pilot with an arbitrary length. Hence, pilot sequence can be designed more flexibly to maximize spectral efficiency.