Large Number Of Base Station Antennas Research Articles

Performance Analysis of Channel Estimation for Massive MIMO Communication Using DL-Based Fully Connected Neural Network (DL-FCNN) Architecture

The latest research for applying deep learning in wireless communications gives several opportunities to reduce complex signal processing. The channel estimation is important to study the nature of the varying channel and to calculate channel state information (CSI) value which is utilized at the receiver to nullify the interference which occurs during multipath transmission. In the current article, considering the massive Multiple Input Multiple Output (MIMO) channel model, a DL approach is developed with a fully connected neural network (NN) architecture which is used to estimate the channel with minimum error. The proposed DL architecture uses an openly available channel dataset. Further, using generated pilot symbols of lengths 2 and 4, the performance of DL-based Fully connected NN (DL-FCNN) is analyzed to estimate the channel in uplink massive MIMO communication. The obtained results demonstrate that the channel estimation performance was calculated in terms of normalized mean square error((NMSE) for different values of SNR added at receiver base station (BS) to the signals over the range of BS antennas. Also, the channel estimation error over a large number of BS antennas for massive MIMO scenarios is observed, and it is observed that the NMSE reduces with a greater number of antennas. Hence, it can be inferred that the DL models will be the future for most physical layer signal processing techniques such as channel estimation, modulation detection, etc. within massive MIMO networks.

Low-Complexity Subspace MMSE Channel Estimation in Massive MU-MIMO System

Massive multi-user multiple-input multiple-output (massive MU-MIMO) technology is considered as a promising enabler to fulfill the rapid growth of traffic requirement for wireless mobile communications. The massive MU-MIMO system can achieve unlimited capacity when the base station (BS) has accurate channel state information (CSI). In time-division-duplex (TDD) mode, the BS estimates CSI by receiving pilot signals sent from user terminals (UEs). However, because of using non-orthogonal pilots, pilot contamination happens to degrade the quality of the CSI estimation. To deal with pilot contamination problem, a low-complexity subspace minimum mean square error (MMSE) estimation method is proposed in this paper. Specifically, our approach operates the MMSE estimation in a low-dimensional subspace to avoid large matrix manipulation. Meanwhile, subspace projection helps to discriminate the desired signal and interfering signals in the power domain. Interference analysis shows the MMSE estimation can achieve interference-free estimation even in a low-dimensional subspace with a large number of BS antennas, and non-overlapping angles of arrival (AoAs) between desired and interfering UEs. Furthermore, thanks to the low-rank property of the channel covariance matrix in massive MU-MIMO systems, a two-step covariance matrix subspace projection method is proposed for further computational complexity reduction. The complexity analysis and simulation results indicate that our proposed approach has better channel estimation accuracy with lower complexity than the conventional MMSE estimation when the number of BS antennas is large.

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC.

An Agile-Beam NOMA Scheme in Millimeter Wave Networks

We propose an agile-beam non-orthogonal multiple access (NOMA) scheme for millimeter wave (mmWave) communication networks. The agile-beam NOMA scheme can flexibly switch between the single/multi-beam NOMA transmission depending on the angular phase difference. In particular, we derive the asymptotic switching point with equal power and antennas resource allocation in a large number of BS antennas, which enables base station (BS) to make preliminary selection on beam mode. Furthermore, the optimization problem of maximizing the achievable sum rate in the agile-beam NOMA scheme is formulated and then decomposed into two subproblems, i.e., maximizing the sum rate in single-beam case and multi-beam case. And the optimal solution of power and antennas allocation is provided. Moreover, the proposed agile-beam NOMA scheme is extended to more general multi-user case. Simulation results verify the effectiveness of the asymptotic switching point compared with exact switching point. With the sum rate obtained by intelligent optimization algorithm as a benchmark, the optimal solution is validated in simulation. In addition, the sum rate with optimal resource allocation achieves significant gain over equal resource allocation.

Spectral Efficiency of Massive MIMO Communication Systems with Zero Forcing and Maximum Ratio Beamforming

The massive multiple-input-multiple-output (MIMO) is a key enabling technology for the 5G cellular communication systems. In massive MIMO (M-MIMO) systems few hundred numbers of antennas are deployed at each base station (BS) to serve a relatively small number of single-antenna terminals with multiuser, providing higher data rate and lower latency. In this paper, an M-MIMO communication system with a large number of BS antennas with zero-forcing beamforming is proposed for the improved spectral efficiency performance of the system. The zero forcing beamforming technique is used to overcome the interference that limits the spectral efficiency of M-MIMO communication systems. The simulation results authenticate the improvement in the spectral efficiency of M-MIMO system. The spectral efficiency value using zero-forcing beamforming is near to the spectral efficiency value with the no-interference scenario.

Channel Feedback Based on AoD-Adaptive Subspace Codebook in FDD Massive MIMO Systems

Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, prior work on multiuser MIMO has shown that the feedback overhead scales linearly with the number of base station (BS) antennas, which is large in massive MIMO systems. To reduce the feedback overhead, we propose an angle-of-departure (AoD) adaptive subspace codebook for channel feedback in FDD massive MIMO systems. Our key insight is to leverage the observation that path AoDs vary more slowly than the path gains. Within the angle coherence time, by utilizing the constant AoD information, the proposed AoD-adaptive subspace codebook is able to quantize the channel vector in a more accurate way. From the performance analysis, we show that the feedback overhead of the proposed codebook only scales linearly with a small number of dominant (path) AoDs instead of the large number of BS antennas. Moreover, we compare the proposed quantized feedback technique using the AoD-adaptive subspace codebook with a comparable analog feedback method. Extensive simulations show that the proposed AoD-adaptive subspace codebook achieves good channel feedback quality, while requiring low overhead.

Downlink Performance of Massive-MIMO Systems Using EVD-Based Channel Estimation

In this paper, we consider the downlink of multicell multiuser massive multiple-input–multiple-output (MIMO) systems employing the eigenvalue-decomposition-based channel estimation. The impacts of two errors are emphasized, namely, the error due to nonorthogonal channel vectors caused by a finite number of base station (BS) antennas and the error of the sample covariance matrix because of the received signal with a finite length. First, we derive the closed-form expressions for the achievable rate and the symbol error rate (SER). It is shown that no matter how large the number of BS antennas is, the rate loss due to finite BS antennas always exists. As the number of BS antennas increases, the rate loss converges to a constant, which only relates to the large-scale factors and transmit power. On the other hand, the rate loss caused by the sample covariance matrix could be zero when the number of received signal goes to infinity. We further investigate the asymptotic behaviors of the SER in the regimes of high signal-to-noise ratio (SNR), large number of received signals, and large number of BS antennas. It is demonstrated that the SER at high SNRs is interference limited instead of noise limited; the boundary of the SER in the limit of an infinite number of received signal is also derived. Although increasing the number of BS antennas can improve system performance, there is always a gap between the SER and that obtained without these two errors. More importantly, this gap enlarges as the number of BS antennas increases. Numerical results are presented to verify our analysis.

Weighted-Graph-Coloring-Based Pilot Decontamination for Multicell Massive MIMO Systems

A multicell massive multiple-input multiple-output (MIMO) system, which utilizes a large number of base-station antennas to simultaneously serve a set of users, suffers from pilot contamination (PC) due to unavoidable reuse of pilots in adjacent cells. In this paper, a weighted-graph-coloring-based pilot decontamination (WGC-PD) scheme is proposed to mitigate PC for multicell massive MIMO systems. Specifically, based on limited cooperation among cells, an edge-weighted interference graph (EWIG) is first constructed to depict the potential PC relationship among users, whereby two users in different cells are connected by a weighted edge, indicating the strength of potential PC when they reuse the same pilot. Then, inspired by classical graph coloring algorithms, we develop the WGC-PD scheme by denoting each color as a pilot and each vertex as a user in the EWIG, which is able to mitigate PC by assigning different pilots to connected users with a large weight in a greedy way with insufficient pilot resource. Compared with exhaustive search among numerous pilot assignment solutions, the proposed WGC-PD scheme is able to mitigate PC with significantly reduced complexity, which is verified by numerical results.

Overview of Full-Dimension MIMO in LTE-Advanced Pro

Multiple-input multiple-output (MIMO) systems with a large number of basestation antennas, often called massive MIMO, have received much attention in academia and industry as a means to improve the spectral efficiency, energy efficiency, and processing complexity of next generation cellular system. Mobile communication industry has initiated a feasibility study of massive MIMO systems to meet the increasing demand of future wireless systems. Field trials of the proof-of-concept systems have demonstrated the potential gain of the Full-Dimension MIMO (FD-MIMO), an official name for the MIMO enhancement in 3rd generation partnership project (3GPP). 3GPP initiated standardization activity for the seamless integration of this technology into current 4G LTE systems. In this article, we provide an overview of the FD-MIMO system, with emphasis on the discussion and debate conducted on the standardization process of Release 13. We present key features for FD-MIMO systems, a summary of the major issues for the standardization and practical system design, and performance evaluations for typical FD-MIMO scenarios.

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.

Antenna Ratio for Sum-Rate Maximization in Full-Duplex Large-Array Base Station With Half-Duplex Multiantenna Users

This paper analyzes the ratio of transmit antennas to receive antennas at the base station (BS) in full-duplex multiuser multiple-input-multiple-output (MU-MIMO) systems with large-array BS and half-duplex multiantenna users. We consider a full-duplex BS with a block diagonalization (BD) precoder for downlink transmission and a BD receive filter for uplink reception. We derive the approximated downlink sum-rate considering the inter-user interference, and the uplink sum-rate considering the self-interference (SI), for large numbers of BS antennas. Based on the analysis, we formulate an optimization problem in terms of the ratio of transmit antennas to receive antennas to maximize the sum-rate. The analysis shows that the antenna ratio for maximizing the sum rate converges to the ratio of downlink streams to uplink streams as the number of total BS antennas goes to infinity. Simulation results confirm the analysis and show that the BS using the derived antenna ratio in the full-duplex MU-MIMO system can achieve about a 10~20 b/s/Hz performance gain compared with the BS using an equal number of transmit and receive antennas.

A Novel Monte-Carlo-Sampling-Based Receiver for Large-Scale Uplink Multiuser MIMO Systems

In this paper, we propose low-complexity algorithms based on Monte Carlo sampling for signal detection and channel estimation on the uplink in large-scale multiuser multiple-input-multiple-output (MIMO) systems with tens to hundreds of antennas at the base station (BS) and a similar number of uplink users. A BS receiver that employs a novel mixed sampling technique (which makes a probabilistic choice between Gibbs sampling and random uniform sampling in each coordinate update) for detection and a Gibbs-sampling-based method for channel estimation is proposed. The algorithm proposed for detection alleviates the stalling problem encountered at high signal-to-noise ratios (SNRs) in conventional Gibbs-sampling-based detection and achieves near-optimal performance in large systems with M-ary quadrature amplitude modulation ( M-QAM). A novel ingredient in the detection algorithm that is responsible for achieving near-optimal performance at low complexity is the joint use of a mixed Gibbs sampling (MGS) strategy coupled with a multiple restart (MR) strategy with an efficient restart criterion. Near-optimal detection performance is demonstrated for a large number of BS antennas and users (e.g., 64 and 128 BS antennas and users). The proposed Gibbs-sampling-based channel estimation algorithm refines an initial estimate of the channel obtained during the pilot phase through iterations with the proposed MGS-based detection during the data phase. In time-division duplex systems where channel reciprocity holds, these channel estimates can be used for multiuser MIMO precoding on the downlink. The proposed receiver is shown to achieve good performance and scale well for large dimensions.

Optimal number of users in zero-forcing based multiuser MIMO systems with large number of antennas

The optimal number of users achieving the maximum sum throughput is analyzed in zero-forcing (ZF) based multiuser multiple-input multiple-output (MIMO) systems with a large number of base station (BS) antennas. By utilizing deterministic ergodic sum rates for the ZF-beamforming (ZF-BF) and ZF-receiver (ZF-R) with a large number of BS antennas [1], [2], we can obtain the ergodic sum throughputs for the ZF-BF and ZF-R for the uplink and downlink frame structures, respectively. Then, we can also formulate and solve the optimization problems maximizing the ergodic sum throughputs with respect to the number of users. This paper shows that the approximate downlink sum throughput for the ZF-BF is a concave function and the approximate uplink sum throughput for the ZF-R is also a concave function in a feasible range with respect to the number of users. The simulation results verify the analyses and show that the derived numbersof users provide the maximum sum throughputs for the ZF-BF as well as ZF-R in multiuser MIMO systems with a large number of BS antennas.