Conventional Massive Massive Multiple-input Multiple-output Research Articles

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.

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.

Pilot Reuse and Interference-Aided MMSE Detection for D2D Underlay Massive MIMO

In uplink device-to-device (D2D) underlay cellular systems, massive multiple-input multiple-output (MIMO) seems promising as the large antenna array at the base station (BS) can nearly null the D2D-to-cellular interference. However, the channel-state information (CSI) from all users is required to obtain this advantage. For the orthogonal training scheme, the pilot overhead increases with the number of D2D users, leading to a loss of the achievable spectral efficiency. In this paper, we propose a pilot scheduling scheme for D2D underlay massive MIMO systems, which allows D2D users to reuse the pilots of cellular users so that the pilot overhead can be reduced. Moreover, a novel interference-aided minimum mean square error (MMSE) detector is proposed to suppress the D2D-to-cellular interference, utilizing the estimated CSI from not only cellular users but D2D users as well. Furthermore, we derive the large-scale approximations of the signal-to-interference-plus-noise ratios (SINRs), which are asymptotically tight and accurate, even for a small number of BS antennas. Based on these SINR approximations, performance analyses are given to provide more insights. Simulation results show that the proposed pilot reuse scheme and detector significantly increase the achievable sum spectral efficiency, as compared with the conventional massive MIMO and other baseline schemes.

On the Multi-User Multi-Cell Massive Spatial Modulation Uplink: How Many Antennas for Each User?

Massive spatial modulation aided multiple-input multiple-output (SM-MIMO) systems have recently been proposed as a novel combination of spatial modulation (SM) and of conventional massive MIMO, where the base station (BS) is equipped with a large number of antennas and simultaneously serves multiple user equipment (UE) that employ SM for their uplink transmission. Since the massive SM-MIMO concept combines the benefits of both the SM and massive MIMO techniques, it has recently attracted substantial research interest. In this paper, we study the achievable uplink spectral efficiency (SE) of a multi-cell massive SM-MIMO system, and derive closed-form expressions to asymptotically lower-bound the SE yielded by two linear BS combining schemes, including maximum ratio (MR) combining and zero forcing (ZF) combining, when a sufficiently large number of BS antennas are equipped. The derivation takes into account the impact of transmitter spatial correlations, of imperfect channel estimations, of user-specific power controls and of different pilot reuse factors. The proposed asymptotic bounds are shown to be tight, even when the scale of BS antennas is limited. The new SE results facilitate a system-level investigation of the optimal number of uplink transmit antennas (TAs) $N$ with respect to SE maximization. Explicitly, we provide theoretical insights on the SE of massive SM-MIMO systems. Furthermore, we demonstrate that massive SM-MIMO systems are capable of outperforming the SE of conventional massive MIMOs relying on single-TA UEs.

Spectral, Energy, and Economic Efficiency of 5G Multicell Massive MIMO Systems With Generalized Spatial Modulation

This paper studies generalized spatial modulation (Gen-SM) schemes in multicell multiuser massive multiple-input multiple-output (MIMO) systems as a promising high-throughput and energy-efficient technique for fifth-generation (5G) wireless networks. A detection algorithm for such systems is proposed based on linear processing techniques. By applying the concept of order statistics, a general framework for approximating the achievable sum rates with linear detection is also given. The probability of detecting antenna combinations is analyzed and is used to approximate the sum-rate performance with practical channel conditions, such as antenna correlation, imperfect channel information, and pilot contamination. The fundamental trade-off between spectral efficiency (SE) and energy efficiency (EE) is also investigated. Despite offering less SE, spatial modulation (SM) with a single active antenna per user is shown to be the most energy-efficient transmission mode among the Gen-SM class. Within the operating range of SM (i.e., in the low-to-moderate SE regime), SM is demonstrated to achieve better EE compared with conventional massive MIMO schemes. The performance in terms of economic efficiency, indicating economic profitability (in monetary unit per second), is also analyzed and is shown to serve as a complementary performance metric, enabling an implicit trade-off between SE and EE.

RF Lens-Embedded Massive MIMO Systems: Fabrication Issues and Codebook Design

In this paper, we investigate a radio frequency (RF) lens-embedded massive multiple-input multiple-output (MIMO) system and evaluate the system performance of limited feedback by utilizing a technique for generating a suitable codebook for the system. We fabricate an RF lens that operates on a 77 GHz (mmWave) band. Experimental results show a proper value of amplitude gain and an appropriate focusing property. In addition, using a simple numerical technique--beam propagation method (BPM)--we estimate the power profile of the RF lens and verify its accordance with experimental results. We also design a codebook--multi-variance codebook quantization (MVCQ)--for limited feedback by considering the characteristics of the RF lens antenna for massive MIMO systems. Numerical results confirm that the proposed system shows significant performance enhancement over a conventional massive MIMO system without an RF lens.

Sectorization based pilot reuse for improving net spectral efficiency in the multicell massive MIMO system

A sectorization method using the uniform circular array (UCA) is proposed to improve the net spectral efficiency (SE) of the multicell massive MIMO system, which is an important index for evaluating the performance of a communication system. We derive the ergodic achievable uplink net SE per cell of a general sectorized system and obtain its deterministic approximation based on the large random matrix theory. Different weight matrices are considered for the sectorized system and the one with the best performance is utilized for further analysis. The consistency of the deterministic approximation with the result of Monte-Carlo simulation is proved at the same time. At last, numerical results indicate that the net SE per cell can be greatly improved compared to the conventional multicell massive MIMO system, which validates the effectiveness of the sectorization method. Moreover, comparisons with other pilot reuse methods are also made in this paper.

MAX-SLNR Precoding Algorithm for Massive MIMO System

Pilot Contamination obviously degrades the system performance of Massive MIMO systems. In this paper, a downlink precoding algorithm based on the Signal-to- Leakage-plus-Noise-Ratio (SLNR) criterion is put forward. First, the impact of Pilot Contamination on SLNR is analyzed，then the precoding matrix is calculated with the eigenvalues decomposition of SLNR, which not only maximize the array gains of the target user, but also minimize the impact of Pilot Contamination and the leak to the users of other cells. Further, a simplified solution is derived, in which the impact of Pilot Contamination can be suppressed only with the large-scale fading coefficients. Simulation results reveal that: in the scenario of the serious pilot contamination, the proposed algorithm can avoid the performance loss caused by the pilot contamination compared with the conventional Massive MIMO precoding algorithm. Thus the proposed algorithm can acquire the perfect performance gains of Massive MIMO system and has better practical value since the large-scale fading coefficients are easy to measure and feedback.

A massive MIMO terminal concept based on small‐size multi‐mode antennas

AbstractIn this tutorial, a novel variation of massive multiple‐input multiple‐output (MIMO) is proposed, dubbed multi‐mode massive MIMO. A key feature is an advanced antenna design suitable for small‐scale user terminals like smartphones. Unlike traditional antenna layouts employing a single‐antenna port per antenna element, multiple orthogonal modes shall be excited on a conductive body, like the printed circuit board. This antenna conception is motivated by the theory of characteristic modes for conductive bodies. Each mode can be assigned with an individual antenna port, and all antenna ports can be fed independently. In conjunction with suitable baseband processing, unique features, such as single‐mode beamforming, can be exploited. Most importantly, some bottlenecks of conventional massive MIMO in conjunction with single‐antenna terminals like single‐stream processing on the uplink and pilot contamination on the downlink can be improved. Performance results for a three‐port prototype multi‐mode antenna support the feasibility of the proposed concept. The terminal concept is suitable in context of the upcoming fifth mobile radio generation (5G) and for ultra‐fast (100 Gbps) wireless internet access among other applications. Copyright © 2015 John Wiley & Sons, Ltd.

Optimized Uplink Transmission in Multi-Antenna C-RAN With Spatial Compression and Forward

Massive MIMO and C-RAN are two promising techniques for implementing future wireless communication systems, where a large number of antennas are deployed either being co-located at the base station (BS) or totally distributed at separate sites called remote radio heads (RRHs). In this paper, we consider a general antenna deployment design for wireless networks, termed multi-antenna C-RAN, where a flexible number of antennas can be equipped at each RRH to more effectively balance the performance and fronthaul complexity trade-off beyond the conventional massive MIMO and single-antenna C-RAN. Under the uplink communication setup, we propose a new "spatial-compression-and-forward (SCF)" scheme, where each RRH first performs a linear spatial filtering to denoise and maximally compress its received signals from multiple users to a reduced number of dimensions, then conducts uniform scalar quantization over each of the resulting dimensions in parallel, and finally sends the total quantized bits to the baseband unit (BBU) via a finite-rate fronthaul link for joint information decoding. Under this scheme, we maximize the minimum signal-to-interference-plus-noise ratio (SINR) of all users at the BBU by a joint resource allocation over the wireless transmission and fronthaul links. Specifically, each RRH determines its own spatial filtering solution in a distributed manner to reduce the signalling overhead with the BBU, while the BBU jointly optimizes the users' transmit power, the RRHs' fronthaul bits allocation, and the BBU's receive beamforming with fixed spatial filters at individual RRHs. Through numerical results, it is shown that given a total number of antennas to be deployed, multi-antenna C-RAN with the proposed SCF and joint optimization significantly outperforms both massive MIMO and single-antenna C-RAN under practical fronthaul capacity constraints.