Conventional Massive Multiple-input Multiple-output Systems Research Articles

Decentralized Coordinated Precoding Design in Cell-Free Massive MIMO Systems for URLLC

Cell-free massive multiple-input multiple-output (MIMO) is a promising network to offer huge improvement of the achievable rate compared with conventional cellular massive MIMO systems. However, the commonly adopted Shannon-type achievable rate is only valid in the long block length regime that is not applicable to the emerging short-packet communication. To realize ultra-reliable and low-latency communication (URLLC) in cell-free massive MIMO systems, we optimize the precoding vector at the access points (APs) to maximize the minimum user rate in both the centralized and decentralized fashion. The design takes into account the impact of URLLC and we propose path-following algorithms (PFA) to address the considered problem which generates a sequence of advanced feasible points and converges to at least a locally optimal solution of the design problem. Moreover, we investigate the requirement of the precoding schemes, the length of the transmission duration, the number of antennas equipped at each AP, and the size of each AP cluster on the URLLC rate. Numerical results show that the decentralized PFA precoding can achieve 80% of the 95%-likely URLLC rate of the centralized precoding and 89% of the average URLLC rate with only 12% computational complexity of the centralized precoding.

A data-driven deep learning network for massive MIMO detection with high-order QAM

Massive multiple-input multiple-output (MIMO) can provide higher spectral efficiency and energy efficiency compared to conventional MIMO systems. Unfortunately, as the numbers of modulation orders and antennas increase, the computational complexity of conventional symbol detection algorithms becomes unaffordable and their performance deteriorates. However, deep learning (DL) techniques can provide flexibility, nonlinearity and computational parallelism for massive MIMO detection to address these challenges. We propose an efficient data-driven detection network, i.e., accelerated multiuser interference cancellation network (AMIC-Net), for uplink massive MIMO systems. Specifically, we first introduce an extrapolation factor regarded as a learnable parameter into the multiuser interference cancellation (MIC) algorithm for iterative sequential detection (ISD) detector through extrapolation technique to enhance the convergence performance. Then we unfold the above accelerated iterative algorithm and adopt a sparsely connected approach, instead of fully connected, to obtain a relatively simple deep neural network (DNN) structure to enhance the detection performance through the data-driven DL approach. Furthermore, in order to accommodate communication scenarios with higher-order modulation, a novel activation function is proposed, which is composed of multiple softsign activation functions with additional learnable parameters to implement a multi-segment mapping of the set of constellation points with different modulations. Numerical results show that the proposed DL network can bring significant performance gain to ISD detector with various massive antenna settings and outperform the existing detectors with the same or lower computational complexity, especially in high-order QAM modulation scenarios.

Is Channel Estimation Necessary to Select Phase-Shifts for RIS-Assisted Massive MIMO?

Reconfigurable intelligent surfaces (RISs) consist of many passive elements of metamaterials whose impedance can be controllable to change the characteristics of wireless signals impinging on them. Channel estimation is a critical task when it comes to the control of a large RIS when having a channel with a large number of multipath components. In this paper, we derive Bayesian channel estimators for two RIS-assisted massive multiple-input multiple-output (MIMO) configurations: i) the short-term RIS configuration based on the instantaneous channel estimates; ii) the long-term RIS configuration based on the channel statistics. The proposed methods exploit spatial correlation characteristics at both the base station and the planar RISs, and other statistical characteristics of multi-specular fading in a mobile environment. Moreover, a novel heuristic for phase-shift selection at the RISs is developed. A computationally efficient fixed-point algorithm, which solves the max-min fairness power control optimally, is proposed. Simulation results demonstrate that the proposed uplink RIS-aided framework improves the spectral efficiency of the cell-edge mobile user equipments substantially in comparison to a conventional single-cell massive MIMO system. The impact of several channel effects are studied to gain insight about when the channel estimation, i.e., the short-term configuration, is preferable in comparison to the long-term RIS configuration to boost the spectral efficiency.

Power Scaling Law Analysis and Phase Shift Optimization of RIS-Aided Massive MIMO Systems With Statistical CSI

This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system, where the phase shifts of the RIS are designed relying on statistical channel state information (CSI). Considering the complex environment, the general Rician channel model is adopted for both the users-RIS links and RIS-BS links. We first derive the closed-form approximate expressions for the achievable rate which holds for arbitrary numbers of base station (BS) antennas and RIS elements. Then, we utilize the derived expressions to provide some insights, including the asymptotic rate performance, the power scaling laws, and the impacts of various system parameters on the achievable rate. We also tackle the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS based on genetic algorithm (GA). Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Our simulations also demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems.

An Attention-Aided Deep Learning Framework for Massive MIMO Channel Estimation

Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL) based ones have exhibited great potential in terms of performance and complexity. In this paper, an attention mechanism, exploiting the channel distribution characteristics, is proposed to improve the estimation accuracy of highly separable channels with narrow angular spread by realizing the “divide-and-conquer” policy. Specifically, we introduce a novel attention-aided DL channel estimation framework for conventional massive MIMO systems and devise an embedding method to effectively integrate the attention mechanism into the fully connected neural network for the hybrid analog-digital (HAD) architecture. Simulation results show that in both scenarios, the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead. Furthermore, strong robustness under different system and channel parameters can be achieved by the proposed approach, which further strengthens its practical value. We also investigate the distributions of learned attention maps to reveal the role of attention, which endows the proposed approach with a certain degree of interpretability.

Low power consumption detectors for mmWave massive MU-MIMO-GFDM systems

ABSTRACT The International Mobile Telecommunications-2020 (IMT-2020) specifies the data rates for enhanced mobile broadband (eMBB) is approximately 10 Gb/s. Millimetre-wave (mmWave) has a vast spectrum and can be used with only slight authorisation, making it an ideal solution for eMBB. This paper proposes optimal detectors with low cost, low power consumption, and low complexity for mmWave massive multi-user multiple-input multiple-output (MU-MIMO) communication. The mmWave broadband communication faces many challenges in system implementation. One of the challenges of mmWave broadband systems is using the analog-to-digital converter (ADC) in the receiver. The ADC power consumption will significantly increase with the frequency bandwidth. The ADC power consumption will also considerably increase with the bit number used in the conversion simultaneously. The mmWave broadband communication also challenges the cost of transmitters, especially wideband linear amplifiers are costly. Hence, it is necessary to transmit a signal with a low peak-to-average power ratio (PAPR) to relax the linearity of the power amplifier. The radio-frequency (RF) port connects to two high-precision ADCs in conventional MIMO systems. Scaling such architectures to massive multiple-input multiple-output (MIMO) with hundreds or thousands of active antenna elements would lead to excessive-high power consumption and hardware costs. Hence, this paper proposes optimal detections for massive multi-user (MU) MIMO-GFDM communications with coarse quantisation. It achieves substantial cost and power savings and provides a high system capacity. The optimal detections for coarse quantisation in large-scale MU-MIMO-GFDM systems have minimal capability loss compared with the ideal case. There is no additional base-band processing complexity. The simulation results show that the SNR gap between the second mismatched quantiser and the precise quantiser is small. When the ratio of receiving and transmitting antennas is the same, the larger the number of antennas, the smaller the SNR difference between the second mismatch quantiser and the precise quantiser.

Power Allocation for Multiple User-Type Massive MIMO Systems

In this paper, we propose a multiple user-type massive multiple-input-multiple-output (MIMO) system and present power allocation solutions for this proposed network. Unlike conventional massive MIMO systems in which all users are treated equally, the proposed system consists of two types of users: quality-of-service (QoS) users, who are guaranteed to have good and stable quality, and non-QoS users, who are similar to users in conventional massive MIMO systems. We formulate two power allocation optimization problems which guarantee quality-of-service for QoS users and maximize the sum-rate of non-QoS users simultaneously. These two problems are solved by two proposed successive convex approximation algorithms, and we show that these two algorithms surely converge to a local maximum Karush-Kuhn-Tucker point. The numerical results show the effectiveness of our proposed power allocation algorithms in multiple user-type massive MIMO systems.

Low-Complexity Near-Optimal Iterative Signal Detection Based on MSD-CG Method for Uplink Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) wireless system is increasingly becoming a vital factor in fifth-generation (5G) communication systems. It is attracting considerable interest due to improve range, spectral efficiency, and coverage as compared to the conventional MIMO systems. In massive MIMO systems, the maximum likelihood detector achieve the optimum performance but it has exponential complexity for realistic antenna configurations systems, Moreover, Linear detectors commonly suffer from a matrix inversion which is not hardware-friendly. There is an increase in the computational complexity associated with the unique benefits of the massive MIMO systems. The system might be classified as an ill-conditioned problem and hence, the signal cannot be detected. To reduce the data detection complexity, we investigate a linear detector based on the multiple search direction conjugate gradient (MSD-CG) in the massive MIMO uplink systems. Several theoretical iterative techniques that can be used to balance complexity and performance for massive MIMO detection have been proposed in the literature. These methods whose convergence rate for common applications is slow where there is a decrease in the base station to user antenna ratio. In this paper, the performance of the CG method has been advanced by a projection method that necessitates a search direction in each sub-domain instead of making all search directions conjugate to each other. In this regard, our results show that the proposed algorithm with realistic antenna configurations is superior to the existing methods in terms of computational complexity for large-scale MIMO systems.

Learned Conjugate Gradient Descent Network for Massive MIMO Detection

In this work, we consider the use of model-driven deep learning techniques for massive multiple-input multiple-output (MIMO) detection. Compared with conventional MIMO systems, massive MIMO promises improved spectral efficiency, coverage and range. Unfortunately, these benefits are coming at the cost of significantly increased computational complexity. To reduce the complexity of signal detection and guarantee the performance, we present a learned conjugate gradient descent network (LcgNet), which is constructed by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes, we explicitly learn their universal values. Also, we can enhance the proposed network by augmenting the dimensions of these step-sizes. Furthermore, in order to reduce the memory costs, a novel quantized LcgNet is proposed, where a low-resolution nonuniform quantizer is integrated into the LcgNet to smartly quantize the aforementioned step-sizes. The quantizer is based on a specially designed soft staircase function with learnable parameters to adjust its shape. Meanwhile, due to fact that the number of learnable parameters is limited, the proposed networks are easy and fast to train. Numerical results demonstrate that the proposed network can achieve promising performance with much lower complexity.

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.

Antenna Selection in TDD Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) system installs a large number of antennas on base station, it requires one radio frequency (RF) chain for each antenna. When the number of antennas is small, the power consumption of the RF chain is negligible compared with the transmitting power. However, with the increase in the number of antennas and the number of RF chains, the ratio of the power consumption of the RF chain to the total power is gradually increasing. Therefore, the energy efficiency (EE) of the system tends to be saturated gradually and decreases with the further increase of antennas. An antenna selection method based on EE in uplink massive MIMO systems is proposed in this paper. Based on the channel state information (CSI) and the 2-norm of channel matrix, the antennas with better CSI are selected for data transmission. By switching of the RF chain among multiple antennas dynamically over time, simulation results show that the EE can be enhanced compared with conventional massive MIMO system, and the trade-off between EE and spectral efficiency (SE) of the system can be achieved.

Cascaded Channel Estimation Technique for Massive MIMO Relay System

This paper concerns with the cascaded channel estimation of massive multiple-input multiple-output (MIMO) relay system. In order to meet the increasing demands for high-speed wireless communication networks, massive MIMO has been recognized as one of the key technologies for the future fifth generation (5G) cellular networks. It is an advanced MIMO technique consists of a very large number of antennas at the base station and serves a smaller number of single-antenna users simultaneously. Basically, the idea of massive MIMO technique is to harvest all the advantages of conventional MIMO system in a much larger scale. To reap the benefits of massive MIMO in practice, an accurate estimation of the channel state information (CSI) is needed. In this paper, the relaying technique has been incorporated with massive MIMO system in order to increase system throughput and improve the coverage in cell-edge users. A relay node is placed in between transmitter and receiver to reduce the path loss and improve the spectral efficiency of massive MIMO system. Cascaded channel estimation technique for massive MIMO relay system is developed in this paper. The mean squared error (MSE) of the cascaded channel estimation for massive MIMO relay sytem is optimized to obtain accurate CSI.

When Quantized Massive MIMO Meets Large MIMO With Higher Order Modulation

In this letter, we compare the capacity of a massive multiple-input multiple-output (MIMO) system using a low-resolution analog-to-digital converter (ADC) and a linear detector against a conventional MIMO system with higher order modulation and near maximum likelihood (ML) detection. We show that in the low-signal-to-noise ratio (SNR) regime, the quantized massive MIMO system can outperform the conventional large MIMO system; however, for high SNR, the conventional MIMO system with a near ML detector can outperform the extreme 1-bit quantized massive MIMO system. An analytical framework that derives the achievable rate of a linear minimum mean-squared error (MMSE)-based detector in a massive MIMO configuration, with the assumptions that the front-end is limited to a low-resolution ADC and channel estimation is imperfect, is presented.

Worst Cell Based Pilot Allocation in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) has been viewed as an advanced technique in future 5G networks. Conventional massive MIMO systems consist of cellular base stations (BS) equipped with a very large number of antennas to simultaneously serve many single-antenna users. Unfortunately, massive MIMO system’s performance is limited by pilot contamination (PC) problem. Conventionally, all users in massive MIMO systems are assigned pilot randomly. In this paper, we propose a pilot allocation algorithm based on a cell with the worst channel quality (WCPA) algorithm to improve the uplink achievable sum rate of the system. Specifically, WCPA exploits the large-scale coefficients of fading channels between the BSs and users. According to the number of available orthogonal pilot sequences, we choose some of the highest inter-cell interfering users and assign each of them a unique pilot sequence if the number of pilot sequences is more than the number of users in a cell. Next, we choose a target cell with the worst channel quality, and gather the highest channel gain user in the target cell and the lowest interfering user in the other cells in the same group in a sequential way by assigning them the same pilot sequence. The simulation results show the outperformance of the proposed algorithm compared to the conventional pilot allocation schemes.

Media-Based Modulation for the Uplink in Massive MIMO Systems

Media-based modulation (MBM) is an attractive modulation scheme, which can achieve high data rates using multiple radio frequency (RF) mirrors (parasitic elements) and fewer transmit antennas/RF chains. In this paper, we consider MBM for the uplink in a massive multiple input multiple output (MIMO) system consisting of tens of users and tens to hundreds of base station receive antennas. Each user employs MBM with one transmit antenna and multiple RF mirrors placed near it. The on / off status of the RF mirrors conveys information bits in addition to the bits conveyed through conventional modulation symbols. We show that the multiuser MBM (MU-MBM) system achieves very good bit error performance compared to other conventional multiuser MIMO systems. We propose two types of low-complexity detection algorithms for massive MU-MBM systems. The first type is based on compressive sensing that exploits the inherent sparse nature of the MBM transmit vectors. The second type is based on message passing that performs both detection and channel estimation at low computational complexity. To do this, it exploits the “channel hardening” phenomenon that occurs in large MIMO channels. It is shown that the proposed algorithms are not only computationally less complex, but also provide very good bit error performance in massive MU-MBM systems.

Massive Connectivity With Massive MIMO—Part II: Achievable Rate Characterization

This two-part paper aims to quantify the cost of device activity detection in an uplink massive connectivity scenario with a large number of devices but device activities are sporadic. Part I of this paper shows that in an asymptotic massive multiple-input multiple-output (MIMO) regime, device activity detection can always be made perfect. Part II of this paper subsequently shows that despite the perfect device activity detection, there is nevertheless significant cost due to device detection in terms of overall achievable rate, because of the fact that non-orthogonal pilot sequences have to be used in order to accommodate the large number of potential devices, resulting in significantly larger channel estimation error as compared to conventional massive MIMO systems with orthogonal pilots. Specifically, this paper characterizes each active user's achievable rate using random matrix theory under either maximal-ratio combining (MRC) or minimum mean-squared error (MMSE) receive beamforming at the base-station (BS), assuming the statistics of their estimated channels as derived in Part I. The characterization of user rate also allows the optimization of pilot sequences length. Moreover, in contrast to the conventional massive MIMO system, the MMSE beamforming is shown to achieve much higher rate than the MRC beamforming for the massive connectivity scenario under consideration. Finally, this paper illustrates the necessity of user scheduling for rate maximization when the number of active users is larger than the number of antennas at the BS.

Vertex Graph-Coloring-Based Pilot Assignment With Location-Based Channel Estimation for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is considered as a promising technique in wireless communication systems, which contains cellular base stations equipped with a very large number of antennas to serve multiple users. However, the performance of massive MIMO is limited by the impact of pilot contamination due to inter-cell interference (ICI). In conventional massive MIMO systems, pilot sequences are randomly assigned to users without any further consideration. In this paper, a vertex graph coloring-based pilot assignment (VGC-PA) algorithm is proposed in conjunction with the existing post-processing discrete Fourier transform (DFT) filtering channel estimation to mitigate the ICI between users with the same pilot sequence in the channel estimation process to improve the capacity of the whole system. Specifically, we propose a metric to measure the potential ICI strength between any two users in the system based on their angle of arrival, correlation, and distances to construct an ICI graph, where each user is regarded as a node. This ICI graph denotes the potential ICI strength relationship among all users in the system. Then, we propose a VGC-PA algorithm to mitigate the ICI relationship between users with the same pilot sequences by assigning different pilots to connected users with high ICI metric based on some criteria. After the pilot assignment process, we apply the existing post-processing DFT filtering in the channel estimation process. The simulation results show the significant improvement of our proposed VGC-PA algorithm compared with existing methods.

Disjoint Pilot Power and Data Power Allocation in Multi-Cell Multi-User Massive MIMO Systems

In this paper, we consider pilot and data power allocation in multi-cell multi-user massive multiple-input multiple-output (MIMO) systems to maximize the summation of spectral efficiency (sum SE). In conventional massive MIMO systems, equal pilot and data power is assigned to all users so the sum SE is not good. A common way to maximize sum SE is forming a maximization problem with the sum SE objective function containing both pilot and data power as variables. However, pilots are only used for channel estimation so pilot power should be allocated based on channel estimation quality instead of sum SE. Moreover, improving significantly channel estimation quality also helps to indirectly improve the sum SE. This motivated us to propose a disjoint pilot and data power allocation where pilot powers are optimized based on the sum normalized mean squared error (sum NMSE) channel estimation minimization problem and data powers are optimized based on sum SE maximization problem. We consider least squares (LS) and minimum mean squared error (MMSE) estimation methods which are commonly used in massive MIMO. In the first step, we formulate a minimization problem to minimize the summation of NMSE of the system under maximum pilot power per user constraint. Minimization problem for MMSE method is non-deterministic polynomial-time (NP) hard so we propose an algorithm to find the local optimum point. First step is only related to pilot powers and indirectly improves the sum SE by improving channel estimation quality. In second step, we further improve the sum SE by formulating a sum SE maximization problem with data powers as variables under maximum data power per user constraint. Since this problem is NP-hard, we derive a lower bound on the sum SE and maximize this bound instead Numerical results show the advantages of our proposed approach in comparison with existing schemes.

Performance Analysis of Dual-Polarized Massive MIMO System with Human-Care IoT Devices for Cellular Networks

The performance analysis of the dual-polarized massive multiple-input multiple-output (MIMO) system with Internet of things (IoT) devices is studied when outdoor human-care IoT devices are connected to a cellular network via a dual-polarized massive MIMO system. The research background of the performance analysis of dual-polarized massive MIMO system with IoT devices is that recently the data usage of outdoor human-care IoT devices has increased. Therefore, the outdoor human-care IoT devices are necessary to connect with 5G cellular networks which can expect 1000 times higher performance compared with 4G cellular networks. Moreover, in order to guarantee the safety of the patient for emergency cases, a human-care Iot device must be connected to cellular networks which offer more stable communication for outdoors compared to short-range communication technologies such as Wi-Fi, Zigbee, and Bluetooth. To analyze the performance of the dual-polarized massive MIMO system for human-care IoT devices, a dual-polarized MIMO spatial channel model (SCM) is proposed which considers depolarization effect between the dual-polarized transmit-antennas and the receive-antennas. The simulation results show that the performance of the dual-polarized massive MIMO system is improved about 16% to 92% for 20 to 150 IoT devices compared to conventional single-polarized massive MIMO system for identical size of the transmit array.

Downlink Achievable Rate Analysis in Massive MIMO Systems With One-Bit DACs

In this letter, we investigate the downlink performance of massive multiple-input multiple-output (MIMO) systems where the base station is equipped with one-bit analog-to-digital/digital-to-analog converters (ADC/DACs). We assume that the base station employs the linear minimum mean-squared-error channel estimator and treats the channel estimate as the true channel to precode the data symbols. We derive an expression for the downlink achievable rate for matched-filter precoding. A detailed analysis of the resulting power efficiency is pursued using our expression of the achievable rate. Numerical results are presented to verify our analysis. In particular, it is shown that, compared with conventional massive MIMO systems, the performance loss in one-bit massive MIMO systems can be compensated for by deploying approximately 2.5 times more antennas at the BS.