510 research outputs found
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
Massive MIMO Performance - TDD Versus FDD: What Do Measurements Say?
Downlink beamforming in Massive MIMO either relies on uplink pilot
measurements - exploiting reciprocity and TDD operation, or on the use of a
predetermined grid of beams with user equipments reporting their preferred
beams, mostly in FDD operation. Massive MIMO in its originally conceived form
uses the first strategy, with uplink pilots, whereas there is currently
significant commercial interest in the second, grid-of-beams. It has been
analytically shown that in isotropic scattering (independent Rayleigh fading)
the first approach outperforms the second. Nevertheless there remains
controversy regarding their relative performance in practice. In this
contribution, the performances of these two strategies are compared using
measured channel data at 2.6 GHz.Comment: Submitted to IEEE Transactions on Wireless Communications,
31/Mar/201
Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems
Channel estimation for the downlink of frequency division duplex (FDD)
massive MIMO systems is well known to generate a large overhead as the amount
of training generally scales with the number of transmit antennas in a MIMO
system. In this paper, we consider the solution of extrapolating the channel
frequency response from uplink pilot estimates to the downlink frequency band,
which completely removes the training overhead. We first show that conventional
estimators fail to achieve reasonable accuracy. We propose instead to use
high-resolution channel estimation. We derive theoretical lower bounds (LB) for
the mean squared error (MSE) of the extrapolated channel. Assuming that the
paths are well separated, the LB is simplified in an expression that gives
considerable physical insight. It is then shown that the MSE is inversely
proportional to the number of receive antennas while the extrapolation
performance penalty scales with the square of the ratio of the frequency offset
and the training bandwidth. The channel extrapolation performance is validated
through numeric simulations and experimental measurements taken in an anechoic
chamber. Our main conclusion is that channel extrapolation is a viable solution
for FDD massive MIMO systems if accurate system calibration is performed and
favorable propagation conditions are present.Comment: arXiv admin note: substantial text overlap with arXiv:1902.0684
Downlink Achievable Rate Analysis for FDD Massive MIMO Systems
Multiple-Input Multiple-Output (MIMO) systems with large-scale transmit antenna arrays, often called massive MIMO, are a very promising direction for 5G due to their ability to increase capacity and enhance both spectrum and energy efficiency. To get the benefit of massive MIMO systems, accurate downlink channel state information at the transmitter (CSIT) is essential for downlink beamforming and resource allocation. Conventional approaches to obtain CSIT for FDD massive MIMO systems require downlink training and CSI feedback. However, such training will cause a large overhead for massive MIMO systems because of the large dimensionality of the channel matrix. In this dissertation, we improve the performance of FDD massive MIMO networks in terms of downlink training overhead reduction, by designing an efficient downlink beamforming method and developing a new algorithm to estimate the channel state information based on compressive sensing techniques. First, we design an efficient downlink beamforming method based on partial CSI. By exploiting the relationship between uplink direction of arrivals (DoAs) and downlink direction of departures (DoDs), we derive an expression for estimated downlink DoDs, which will be used for downlink beamforming. Second, By exploiting the sparsity structure of downlink channel matrix, we develop an algorithm that selects the best features from the measurement matrix to obtain efficient CSIT acquisition that can reduce the downlink training overhead compared with conventional LS/MMSE estimators. In both cases, we compare the performance of our proposed beamforming method with traditional methods in terms of downlink achievable rate and simulation results show that our proposed method outperform the traditional beamforming methods
Joint Port Selection Based Channel Acquisition for FDD Cell-Free Massive MIMO
In frequency division duplexing (FDD) cell-free massive MIMO, the acquisition
of the channel state information (CSI) is very challenging because of the large
overhead required for the training and feedback of the downlink channels of
multiple cooperating base stations (BSs). In this paper, for systems with
partial uplink-downlink channel reciprocity, and a general spatial domain
channel model with variations in the average port power and correlation among
port coefficients, we propose a joint-port-selection-based CSI acquisition and
feedback scheme for the downlink transmission with zero-forcing precoding. The
scheme uses an eigenvalue-decomposition-based transformation to reduce the
feedback overhead by exploring the port correlation. We derive the sum-rate of
the system for any port selection. Based on the sum-rate result, we propose a
low-complexity greedy-search-based joint port selection (GS-JPS) algorithm.
Moreover, to adapt to fast time-varying scenarios, a supervised deep
learning-enhanced joint port selection (DL-JPS) algorithm is proposed.
Simulations verify the effectiveness of our proposed schemes and their
advantage over existing port-selection channel acquisition schemes.Comment: 30 pages, 9 figures. The paper has been submitted to IEEE journal for
possible publicatio
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