Massive Multiple-input Multiple-out Research Articles

Bit Error Rate Analysis Of Multiuser Massive MIMO Wireless System Using Linear Precoding

This paper presents bit error rate (BER) analysis of multiuser (MU) massive multiple input multiple out (MIMO) wireless system using linear precoding techniques. Considering the increasing demand for wireless communication that will provide seamless performance to meet users satisfaction, various precoding schemes have been developed and with massive MIMO projected to be a promising technology for 5G and next generation network. Therefore, this study was basically designed to examine the effect of linear zero forcing (ZF) and minimum mean square error (MMSE) precoders on BER performance of MU massive MIMO system with up to 32 base station antennas (BS) and communicating with up to 5 user mobile terminals (MTs). A model that describes downlink operation of MU massive MIMO wireless communication system with spatial multiplexing employing linear ZF and MMSE precoders such that each user is equipped with single antenna MT resulting in overall access points of 5 antennas was developed in MATLAB. Computer simulations revealed that ZF outperformed MMSE. This observation was validated by similar report on massive MIMO in previous study.

Massive MIMO Empowered Wireless Powered Sensor Networks: An Optimal Design With Statistical CSI

Wireless energy transfer (WET) has been anticipated as a viable solution to combat the challenges of scarce energy supply in Internet-of-Things (IoT) applications. To reap the benefit brought by large scale antenna arrays, massive multiple-input-multiple-out (MIMO) empowered wireless powered sensor networks (WPSNs) is investigated in this letter to meet the urgent demand for reliable and self-sustainable IoT applications. Considering a large-scale antenna array deployed at the access point for energy transfer to and information collection from the multiple-antenna wireless powered sensors, the hardening property in energy transfer is generalized and theoretically proved based on random matrix theory. Based on the hardening properties, the harvesting energy and the uplink sum-rate for sensor data collection are found to be asymptotically deterministic and depend on the statistical channel state information (CSI) only. The hardening properties in both energy and information transfers enable a novel optimal design on the WPSNs based on statistical CSI only. Particularly, the designs of the downlink energy beamforming and multiple uplink information beamforming can be decoupled, and global optimal solutions for the beamforming and time allocation to maximize the uplink sum-rate given downlink power constraint are found in semi-closed forms. Different from prior WPSNs designs, statistical CSI but not instantaneous CSI is exploited and frequent channel estimation and heavy communication overhead can be avoided with a much lower computation complexity, thus improving the practicability and sustainability of the WPSNs. The effectiveness of the proposed design is finally demonstrated by numerical simulations.

Low Complexity Beam Selection Scheme Based on Estimated CSI

The beam selection technology based on the discrete lens array (DLA) can greatly reduce the number of RF chains required by mmWave massive multiple-input multiple-out (MIMO) system. However, the performance of the existing beam selection schemes is on the premise of perfect channel state information (CSI). While in practical situation, accurate channel state information must be obtained by the base station before beam selection. In this paper, the channel estimation scheme based on support detection (SD) is used to estimate the state information of large-scale channel by utilizing the structural characteristics of beamspace. Then on the basis of group-iteration (GI) algorithm, we propose the group-iteration beam selection scheme based on estimated CSI. The simulation results show that this scheme can achieve the performance close to beam selection scheme based on perfect CSI, and compared with the traditional beam selection schemes, the proposed scheme also shows performance advantages.

Joint Beamforming and User Association Scheme for Full-Dimension Massive MIMO Networks

In this paper, we study the joint cooperative beamforming and user association scheme for multi-cell multi-user full-dimension massive multiple-input multiple-out (MIMO) networks, where each user equipment is dynamically associated with one of the base stations. We propose to maximize the network capacity by jointly optimizing the beamforming vectors and a binary vector that represents the user association decisions. The binary vectors are further transformed into binary beam association factors (BAFs) as indicators of both the beamforming and the user association decisions. A three-step Gaussian belief propagation (GaBP) based distributed solver is proposed to solve the non-convex NP-hard problem considered in this paper. By relaxing the binary BAFs, we first transform the optimization problem into a linear programming. Subsequently, GaBP is used to efficiently obtain a feasible solution on BAFs in parallel, and two mapping algorithms are proposed to achieve different performance-complexity tradeoffs. Simulation results show that the proposed cooperative beamforming methods significantly outperform the benchmarks in the literature in terms of the network capacity with a low computation complexity.

An effective localization method for mixed far-field and near-field strictly non-circular sources

In this paper, an effective direction-of-arrival (DOA) and range estimations method for mixed far-field and near-field non-circular sources is proposed based on a large centrosymmetric uniform linear array (ULA). By exploiting the non-circularity of the sources, an extended signal is generated by concatenating the received array data and its conjugate counterparts. Then the DOAs of far-field signals are estimated based on the extended covariance matrix with the traditional MUSIC algorithm. After eliminating the far-field components from the extended signal subspace, the extended covariance matrix of the near-field signals is obtained. Thus a near-field estimator is constructed based on symmetric property of the extended array manifold where the generalized ESPRIT method is adopted to estimate the DOAs of near-field sources. Finally, the range estimator is derived using the DOA estimations of near-field sources. Simulation results are provided to validate that the proposed method has achieved a better performance than existing ones and is quite suitable for massive MIMO (multiple-input multiple-out) system.

Spectral Efficiency of Full-Duplex Wireless Backhaul with Hardware Impaired Massive MIMO for Heterogeneous Cellular Networks

The paper analyzes the sum spectral efficiency (SE) for a heterogeneous cellular network (HetNet) which has the backhaul, provided with wireless full-duplex massive multiple-input multiple-out (MIMO) with hardware distortions. We derive approximate expressions to obtain the uplink/downlink sum SE of the backhaul. The analytic results have been shown to be exact when compared to Monte Carlo simulations. From the analysis, it is shown that the desired signal and the hardware distortion noise have the same order. The sum SE generally improves when the number of receive antennas increases but degrades when the hardware quality reduces. A sum SE performance ceiling is introduced by the hardware quality level.

Large random matrix‐based channel estimation for massive MIMO‐OFDM uplink

This study investigates the benefits offered by random matrix theory (RMT) towards the design of reliable channel estimation algorithms for a multi-user massive multiple-input multiple-out (MIMO)-orthogonal frequency-division multiplexing uplink. Assuming no a priori knowledge of channel statistics (KCS) at the massive base station, the authors propose RMT-aided minimum mean square estimation (MMSE) and RMT-aided sparse Bayesian learning (SBL) approaches for massive channel estimation. These approaches render efficient channel estimates, as illustrated through mean square error (MSE) performance, extracted via Monte-Carlo simulations. The results also show that with increasing antennas at the base station, MSE from the RMT-aided MMSE approach decreases, suggesting its aptness to massive MIMO systems. To further enhance the MSE performance, the MMSE and SBL estimated channel impulse responses are pruned using threshold computed from RMT analysis. The authors characterise MSE degradation due to the randomness in the threshold, with the help of the Marcenko-Pastur law-based non-asymptotic framework and concentration inequalities. Analysis results show that, for channels with approximate sparse common support, this MSE degradation is quite insignificant. Altogether, the study demonstrates that RMT analysis is competent in improving channel estimation at a massive MIMO system, when a priori KCS is completely unavailable.

Near-Optimal Pilot Signal Design for FDD Massive MIMO System: An Energy-Efficient Perspective

This paper investigates the pilot signal design for a massive multiple-input multiple-out (MIMO) frequency division duplexing downlink system by taking the channel spatial correlation into account. Our objective is to optimize the pilot pattern, including the pilot power and structure, from the perspective of energy-efficient communication, which can evaluate the channel estimation accuracy, spectral efficiency, and power consumption simultaneously. The original problem is established based on maximizing the energy efficiency (EE) with a predefined quality-of-service requirement and the total power budget, where the involved cost function is in a nonanalytic form with the non-convex nature. To solve it, an analytical expression is derived first by the virtue of the deterministic equivalent approximation technology, which is shown to be a tight approximation. Based on this, the structure of the EE maximization-based pilot signal matrix is proved to be column orthogonal, where the column corresponds to the dominant eigen-directions of channel spatial correlation matrix. By use of the derived pilot structure, the primal non-convex fractional optimization problem is recast to an equivalent optimization problem with the objective function in a subtractive form, which can be solved by deliberately manipulating the Lagrangian function. Finally, an iterative algorithm is proposed to obtain the closed-form solution and further insights are extracted. Numerical results validate the performance gain of our proposed near-optimal pilot scheme in terms of the energy efficiency and spectral efficiency compared with the classical mean squared error-based pilot scheme.

A non-stationary channel model for 5G massive MIMO systems

We propose a novel channel model for massive multiple-input multiple-out (MIMO) communication systems that incorporate the spherical wave-front assumption and non-stationary properties of clusters on both the array and time axes. Because of the large dimension of the antenna array in massive MIMO systems, the spherical wave-front is assumed to characterize near-field effects resulting in angle of arrival (AoA) shifts and Doppler frequency variations on the antenna array. Additionally, a novel visibility region method is proposed to capture the non-stationary properties of clusters at the receiver side. Combined with the birth-death process, a novel cluster evolution algorithm is proposed. The impacts of cluster evolution and the spherical wave-front assumption on the statistical properties of the channel model are investigated. Meanwhile, corresponding to the theoretical model, a simulation model with a finite number of rays that capture channel characteristics as accurately as possible is proposed. Finally, numerical analysis shows that our proposed non-stationary channel model is effective in capturing the characteristics of a massive MIMO channel.

Planning Wireless Cellular Networks of Future: Outlook, Challenges and Opportunities

Cell planning (CP) is the most important phase in the life cycle of a cellular system as it determines the operational expenditure, capital expenditure, as well as the long-term performance of the system. Therefore, it is not surprising that CP problems have been studied extensively for the past three decades for all four generations of cellular systems. However, the fact that small cells, a major component of future networks, are anticipated to be deployed in an impromptu fashion makes CP for future networks vis-a-vis 5G a conundrum. Furthermore, in emerging cellular systems that incorporate a variety of different cell sizes and types, heterogeneous networks (HetNets), energy efficiency, self-organizing network features, control and data plane split architectures (CDSA), massive multiple input multiple out (MIMO), coordinated multipoint (CoMP), cloud radio access network, and millimetre-wave-based cells plus the need to support Internet of Things (IoT) and device-to-device (D2D) communication require a major paradigm shift in the way cellular networks have been planned in the past. The objective of this paper is to characterize this paradigm shift by concisely reviewing past developments, analyzing the state-of-the-art challenges, and identifying future trends, challenges, and opportunities in CP in the wake of 5G. More specifically, in this paper, we investigate the problem of planning future cellular networks in detail. To this end, we first provide a brief tutorial on the CP process to identify the peculiarities that make CP one of the most challenging problems in wireless communications. This tutorial is followed by a concise recap of past research in CP. We then review key findings from recent studies that have attempted to address the aforementioned challenges in planning emerging networks. Finally, we discuss the range of technical factors that need to be taken into account while planning future networks and the promising research directions that necessitates the paradigm shift to do so.

Sounding resource management for QoS support in massive MIMO systems

In massive multiple-input multiple-out (M-MIMO) systems, the base station estimates each forward-link channel by using the reverse-link sounding pilot and the channel reciprocity property of time division duplex (TDD) operation. However, conventional non-cooperative sounding schemes, like a sounding sequence assignment scheme with reuse factor-1, cause cell edge users to suffer from pilot contamination in multi-cell scenarios. The pilot contamination problem becomes even worse in an environment where mobile users are travelling through cell edge areas frequently. To alleviate this problem, a cooperative sounding resource reuse method that assigns each cell a set of resources different from other neighboring cells by using a reuse factor-3 scheme, can be considered, but it significantly lowers the number of served users. In this paper, we propose a partial sounding resource reuse (PSRR) method which aims to seamlessly support the quality of service (QoS) of each mobile user by mitigating the pilot contamination and to minimize the reduction in the number of served users. To this end, the PSRR divides each cell area into center and edge areas, and applies a reuse factor-1 scheme for center users while a reuse factor-3 scheme for edge users. We use a Markov chain model to analyze the performance of the PSRR, and evaluate the accuracy of our analysis through simulations. Then we confirm that the PSRR considerably improves QoS performance over the conventional competitive schemes.