Multi-cell Massive MIMO Systems Research Articles

Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems With Low-Resolution Data Converters

Coordinated Per-Antenna Power Minimization for Multicell Massive MIMO Systems With Low-Resolution Data Converters

Fighting Against Active Eavesdropper: Distributed Pilot Spoofing Attack Detection and Secure Coordinated Transmission in Multi-cell Massive MIMO Systems

Fighting Against Active Eavesdropper: Distributed Pilot Spoofing Attack Detection and Secure Coordinated Transmission in Multi-cell Massive MIMO Systems

Grant-Free Random Access in Multicell Massive MIMO Systems with Mixed-Type Devices: Backoff Mechanism Optimizations under Delay Constraints

Grant-Free Random Access in Multicell Massive MIMO Systems with Mixed-Type Devices: Backoff Mechanism Optimizations under Delay Constraints

On the Coexistence of eMBB and URLLC in Multi-Cell Massive MIMO

The non-orthogonal coexistence between the enhanced mobile broadband (eMBB) and the ultra-reliable low-latency communication (URLLC) in the downlink of a multi-cell massive MIMO system is rigorously analyzed in this work. We provide a unified information-theoretic framework blending an infinite-blocklength analysis of the eMBB spectral efficiency (SE) in the ergodic regime with a finite-blocklength analysis of the URLLC error probability relying on the use of mismatched decoding, and of the so-called saddlepoint approximation. Puncturing (PUNC) and superposition coding (SPC) are considered as alternative downlink coexistence strategies to deal with the inter-service interference, under the assumption of only statistical channel state information (CSI) knowledge at the users. eMBB and URLLC performances are then evaluated over different precoding techniques and power control schemes, by accounting for imperfect CSI knowledge at the base stations, pilot-based estimation overhead, pilot contamination, spatially correlated channels, the structure of the radio frame, and the characteristics of the URLLC activation pattern. Simulation results reveal that SPC is, in many operating regimes, superior to PUNC in providing higher SE for the eMBB yet achieving the target reliability for the URLLC with high probability. Moreover, PUNC might cause eMBB service outage in presence of high URLLC traffic loads. However, PUNC turns to be necessary to preserve the URLLC performance in scenarios where the multi-user interference cannot be satisfactorily alleviated.

On Rate-Splitting With Non-Unique Decoding in Multi-Cell Massive MIMO Systems

We consider the downlink of a multi-cell massive MIMO system suffering from asymptotic rate saturation due to pilot contamination. As opposed to treating pilot contamination interference as noise (TIN), we study the performance of decoding the pilot contamination interference. We model pilot-sharing users as an interference channel (IC) and study the performance of schemes that decode this interference partially based on rate-splitting (RS), and compare the performance to schemes that decode the interference in its entirety based on simultaneous unique decoding (SD) or non-unique decoding (SND). For RS, we non-uniquely decode each layer of the pilot contamination interference and use one common power splitting coefficient per IC. Additionally, we establish an achievable region for this RS scheme. Solving a maximum symmetric rate allocation problem based on linear programming (LP), we show that for zero-forcing (ZF) with spatially correlated/uncorrelated channels and with a practical number of BS antennas, RS achieves significantly higher spectral efficiencies than TIN, SD and SND. Furthermore, we numerically examine the impact of increasing the correlation of the channel across antennas, the number of users as well as the degree of shadow fading. In all cases, we show that RS maintains significant gain over TIN, SD and SND.

Energy Efficiency Optimization of Massive MIMO System with Uplink Multi-Cell Based on Imperfect CSI with Power Control

In order to solve the energy efficiency optimization problem in the uplink multi-cell massive MIMO system, this paper constructs the system transmission model, of which the channel is symmetry, based on user and base station, and deduces the expression of data transmission rate of each user. Then, we establish a model of the spectral and energy efficiency of multi-cell massive MIMO system by analyzing the pilot transmission and channel estimation. We also derive the nonconvex function for the energy efficiency optimization, which is difficult to solve directly. Therefore, we propose an improved particle swarm optimization algorithm to obtain the suboptimal solution, under low complexity, by optimizing the distribution of user power. To demonstrate the advantages of our proposed algorithm, we simulate the energy efficiency performance of the algorithm. The results show that the proposed algorithm can effectively improve the energy efficiency of the system.

Pilot Contamination Suppression Based Coordination in Multi-cell Massive MIMO Systems

Pilot Contamination Suppression Based Coordination in Multi-cell Massive MIMO Systems

Ant Colony-based Optimization algorithm to overcome the pilot contamination issue within multi-cell Massive MIMO systems

the pilot contamination (PC) issue still faces and limits the promising massive MIMO (mMIMO) technology, to unleash the high benefits of mMIMO, we provide here a new decontaminating strategy, which exploits the large-scale fading coefficients' characteristics to construct a search space for the Ant colony-based optimization (ACO) algorithm. This algorithm is employed to find the best pattern in which each UE is linked to its most concurrent UEs of the adjacent cells; specifically, each UEs has an enemy UEs that if they are allocated with the same pilot, the severity of the PC upon the two UEs become subversive for the quality-of-service of the two UEs. Hence, the ACO algorithm finds for each UE its enemy UE, which leads to construct a Hamiltonian graph. This graph is exploited during the assignment of the pilot sequences to the overall UEs; specifically, the linked UEs are successively allocated with the available OPSs, which leads to address the PC problem within multi-cell mMIMO systems.

A Novel Adaptive Channel Allocation Scheme for Multi-Cell Massive MIMO Systems

A Novel Adaptive Channel Allocation Scheme for Multi-Cell Massive MIMO Systems

Cell partitioned M-QAM for semi-blind uplink interference suppression on multicell massive MIMO systems

Cell partitioned M-QAM for semi-blind uplink interference suppression on multicell massive MIMO systems

Performance Analysis of TDD Multicell Massive MIMO Systems With Non-Orthogonal Pilots and Hardware Imperfections in Rician Fading Channels

The performance of a time-division duplex (TDD) multicell massive MIMO ( $\mathrm{M}^2$ -MIMO) system with non-orthogonal pilots (Non-OPs) for channel estimation (CE) and hardware imperfections (HWIs), i.e., low-precision ADCs/DACs at the base station (BS) and non-ideally operating user equipment (UE) distorted by RF chain impairments (RF-CIs), is analyzed and evaluated. Assuming that this communication system operates in a Rician fading channel, firstly, the minimum mean square error (MMSE) CE complexity for both Non-OPs and orthogonal pilots (OPs) are derived and compared. It is also shown that the variance of the MMSE CE error with Non-OPs can be approximated as a linear fraction. Based on this CE analysis, the uplink (UL) and downlink (DL) spectral efficiency (SE) performances with ideal and non-ideal channel state information (CSI) are derived. Furthermore, the UL-SE performances for both Non-OPs and OPs are compared to show the advantage of using Non-OPs for applications which have very high connectivity requirements. The power scaling law is also used to obtain additional insights on how these HWIs affect the overall $\mathrm{M}^2$ -MIMO SE performance. In order to balance the total BS circuitry power consumption against UL-SE, the tradeoff between UL energy efficiency (EE) and UL-SE has been investigated by analyzing the performance of several receive detection strategies as a function of the employed ADC quantization bits (Q-bits). Moreover, the optimal ADC/DAC Q-bit region which maximizes the DL-EE is identified. Various performance evaluation results obtained by means of computer simulations have verified the analysis and validated the accuracy of the equivalent theoretical performance evaluation results.

Semi-Blind Channel Estimation and Data Detection for Multi-Cell Massive MIMO Systems on Time-Varying Channels

We study the problem of semi-blind channel estimation and symbol detection in the uplink of multi-cell massive MIMO systems with spatially correlated time-varying channels. An algorithm based on expectation propagation (EP) is developed to iteratively approximate the joint a posteriori distribution of the unknown channel matrix and the transmitted data symbols with a distribution from an exponential family. This distribution is then used for direct estimation of the channel matrix and detection of data symbols. A modified version of the popular Kalman filtering algorithm referred to as KF-M emerges from our EP derivation and it is used to initialize the EP-based algorithm. Performance of the Kalman smoothing algorithm followed by KF-M is also examined. Simulation results demonstrate that channel estimation error and the symbol error rate (SER) of the semi-blind KF-M, KS-M, and EP-based algorithms improve with the increase in the number of base station antennas and the length of the transmitted frame. It is shown that the EP-based algorithm significantly outperforms KF-M and KS-M algorithms in channel estimation and symbol detection. Finally, our results show that when applied to time-varying channels, these algorithms outperform the algorithms that are developed for block-fading channel models.

Energy-Efficient Massive MIMO With Decentralized Precoder Design

This paper presents an energy-efficient downlink precoding scheme in a multi-cell Massive MIMO system. We approach the precoder design problem to maximize the system energy efficiency by jointly considering power control, interference management, antenna switching and user throughput in a cluster of base stations. This is computationally difficult as it requires solving a sparsity-inducing non-convex optimization problem, which is NP-hard. To alleviate the solution complexity, first a stochastic smooth approximation of zero-norm is applied in the antenna power management to enable fast, gradient-based algorithms. For efficient convergence, we develop a novel optimization algorithm combining augmented multiplier (AM) and quadratic programming (QP), and show how this scheme permits decentralized implementation by offloading parts of the computation to the individual base stations to reduce communication overhead. We provide theoretical proof that the proposed algorithm converges both locally and globally under realistic assumptions. Numerical results confirm that our method achieves higher energy efficiency with a superior convergence rate compared to different types of existing methods, and illustrate the relationship between energy efficiency performance and system design parameters.

Pareto-Optimal Pilot Design for Cellular Massive MIMO Systems

We introduce a non-orthogonal pilot design scheme that simultaneously minimizes two contradicting targets of channel estimation errors of all base stations (BSs) and the total pilot power consumption of all users in a multi-cell massive MIMO system, subject to the transmit power constraints of the users in the network. We formulate a multi-objective optimization problem (MOP) with two objective functions capturing the contradicting targets and find the Pareto optimal solutions for the pilot signals. Using weighted-sum-scalarization technique, we first convert the MOP to an equivalent single-objective optimization problem (SOP), which is not convex. Assuming that each BS is provided with the most recent knowledge of the pilot signals of the other BSs, we then decompose the SOP into a set of distributed non-convex optimization problems to be solved at individual BSs. Finally, we introduce an alternating optimization approach to cast each one of the resulting distributed optimization problems into a convex linear matrix inequality (LMI) form. We provide a mathematical proof for the convergence of the proposed alternating approach and a complexity analysis for the LMI optimization problem. Simulation results confirm that the proposed approach can significantly reduce pilot power, whilst maintaining the same level of channel estimation error as in a baseline.

Efficient Techniques for In-Band System Information Broadcast in Multi-Cell Massive MIMO

We consider joint beamforming of data to scheduled terminals (STs) and broadcast of system information (SI) to idle terminals (ITs) on the same time-frequency resource in multi-cell multi-user massive MIMO systems. We consider two different types of SI broadcast, i) synchronous broadcast of common (i.e., same) SI symbols from all cells, and ii) synchronous broadcast of cell-specific SI symbols from each cell. Through analysis we derive expressions for the achievable sum rate to STs in each cell and the rate of SI transmission to an IT for both these types of SI broadcast. We also derive expressions for the sum rate to STs and the rate to an IT for traditional orthogonal access (OA) where a fraction of physical resource is reserved for broadcast of SI. Simulations reveal that, just as in the single-cell scenario, for the multi-cell scenario also, joint beamforming and broadcasting (JBB) is more energy efficient than OA.

Performance analysis of linear precoders with imperfect channel covariance information for multicell system

Comparison and analysis of the regularised zero forcing precoder, rapid numerical algorithms-based precoder and the truncated polynomial expansion-based precoder are done for massive multiple-input multiple-output wireless system for multicell scenario. The analysis was done for the imperfect channel covariance information. The achievable signal-to-interference-and-noise ratio, spectral efficiency and energy efficiency were investigated. The simulated outcome of the rapid numerical algorithms, regularised zero forcing and truncated polynomial expansion precoders for multicell massive MIMO system was analysed. The rapid numerical algorithms-based precoder gave the best performance followed by the regularised zero forcing precoder, and the truncated polynomial expansion-based precoder had the lowest performance for the multicell massive MIMO system. The increase in spectral efficiency per cell can be attributed to the fact that the pre-log factor reduces with the increased number of pilots. Also, this leads to increased instantaneous signal-to-interference-and-noise ratio as the channel estimates become better with reduced pilot contamination. Again, for truncated polynomial expansion precoding there is a reduction in spectral efficiency because improvement in approximation quality do not overshadow the reduction in pre-log factor. The performance is evaluated for uncoordinated and coordinated massive MIMO.

Pilot Design and Allocation for Multi-Cell Massive MIMO Systems With Two-Tier Receiving

This paper investigates pilot design and allocation in multi-cell massive multiple-input multiple-output systems with two-tier receiving. As the length of pilot is limited, the pilots cannot be orthogonal and cause interference. The sum rate as well as the signal-to-interference-plus-noise ratio (SINR) of the system are firstly analyzed. The sum rate upper bound and the asymptotic SINR are derived. The analysis shows that the pilot correlation and the spatial correlation jointly affect the sum rate. Based on this observation, the discrete Fourier transform vectors are employed in designing pilots with various correlation values. Then, with the channel correlation information and the designed pilots, the pilots are allocated to the cells in a greedy way with the object of maximizing the sum rate upper bound. Moreover, the sum rate analysis, the pilot design, and the pilot allocation are extended to the scenario with grouped correlations. Simulation results show that the proposed pilot design and allocation performs better than other pilots and the derived upper bound is effective.

Two dimension angle of arrival-based precoding for pilot contamination reduction in multi-cell massive MIMO systems

This paper proposes a new precoding approach that utilizes two-dimension (2D) Angle of Arrival (AOA) based precoding (beamforming) approach for multi-cell massive Multiple Input Multiple Output (m-MIMO) systems. The proposed approach enhances the performance of m-MIMO systems by overcoming pilot contamination that is caused by corrupted Channel State Information. It exploits the spatial properties of the propagated signals to distinguish between various users in the multi-cell m-MIMO system and selects the partial space for beamforming based on AOA information to suppress inter-cell and intra-cell interference. We mathematically analyze the impact of pilot contamination in the uplink training and determine the closed-form approximation of uplink Minimum Mean Square Error (MMSE) estimator. By using MMSE estimator as well as 2D AOA and channel estimation, a corresponding MMSE precoding which minimizes the effect of pilot contamination and improves the downlink achievable rate of the system is proposed. Furthermore, a comparison is made of the resolution between the various 2D AOA estimations for an m-MIMO and the optimum values closest bound for the proposed precoding was chosen to reduce pilot contamination. The performance of the proposed MMSE precoding based on 2D AOA estimation methods: 2D Unitary Estimation of Signal Parameters via Rotational Invariance Techniques (2D UESPRIT), 2D Fourier Domain Line Search MUSIC (2D FDLSM), and 2D Propagator Method (2D PM)) were evaluated and compared with pilot contaminated system and deterministic MMSE precoding in terms of their respective achievable rate. Simulation results reveal that the achievable sum rate gains of 2D UESPRIT, 2D FDLSM, and 2D PM based precoding which achieved 97.6%, 92.4%, 86.3% of the desired deterministic performance, respectively, compared to pilot contaminated system, which achieved only 90% even when in the best case of having only one contaminated user out of all users within the activated cell. Findings from our research indicate the feasibility of utilizing the AOA based beamforming to reduce pilot contamination effect that is inherent in a larger order m-MIMO system for the Fifth Generation (5G) wireless transmissions.

Capacity Scaling of Massive MIMO in Strong Spatial Correlation Regimes

This paper investigates the capacity scaling of multicell massive MIMO systems in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the "random sparse angular support" model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. To achieve the asymptotic capacity results, statistical spatial despreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users scales linearly with base station antennas, a linear growth of the capacity with respect to the number of antennas is achievable under the sparse angular support model. We note that the achievable rate lower bound based on massive MIMO "channel hardening", widely used in the massive MIMO literature, yields rather loose results in the strong spatial correlation regimes and may significantly underestimate the achievable rate of massive MIMO. This work therefore considers an alternative bounding technique which is better suited to the strong correlation regimes. In fading channels with sparse angular support, it is further shown that spatial despreading (spreading) in uplink (downlink) has a more prominent impact on the performance of massive MIMO than channel hardening.

Adaptive Pilot Allocation for Multi-cell Massive MIMO Systems

In the upcoming 5G system, the massive MIMO technology will perform an important role in significantly improving the system performance. To sufficiently exploit the potential of massive MIMO, the transceiver should perform appropriate accurate channel estimation, including carefully designed quantities of pilot resource and its allocation among different users. In this letter, a low-complexity adaptive pilot allocation algorithm using the second-order channel statistics of all users is proposed, which helps in finding a good balance between the allocated pilot length and pilot training interference. Numerical results indicate that the proposed algorithm can significantly improve the net spectrum efficiency in multi-cell massive MIMO systems.