799 research outputs found
Massive MIMO has Unlimited Capacity
The capacity of cellular networks can be improved by the unprecedented array
gain and spatial multiplexing offered by Massive MIMO. Since its inception, the
coherent interference caused by pilot contamination has been believed to create
a finite capacity limit, as the number of antennas goes to infinity. In this
paper, we prove that this is incorrect and an artifact from using simplistic
channel models and suboptimal precoding/combining schemes. We show that with
multicell MMSE precoding/combining and a tiny amount of spatial channel
correlation or large-scale fading variations over the array, the capacity
increases without bound as the number of antennas increases, even under pilot
contamination. More precisely, the result holds when the channel covariance
matrices of the contaminating users are asymptotically linearly independent,
which is generally the case. If also the diagonals of the covariance matrices
are linearly independent, it is sufficient to know these diagonals (and not the
full covariance matrices) to achieve an unlimited asymptotic capacity.Comment: To appear in IEEE Transactions on Wireless Communications, 17 pages,
7 figure
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
Low-Complexity Channel Estimation in Large-Scale MIMO using Polynomial Expansion
This paper considers pilot-based channel estimation in large-scale
multiple-input multiple-output (MIMO) communication systems, also known as
"massive MIMO". Unlike previous works on this topic, which mainly considered
the impact of inter-cell disturbance due to pilot reuse (so-called pilot
contamination), we are concerned with the computational complexity. The
conventional minimum mean square error (MMSE) and minimum variance unbiased
(MVU) channel estimators rely on inverting covariance matrices, which has cubic
complexity in the multiplication of number of antennas at each side. Since this
is extremely expensive when there are hundreds of antennas, we propose to
approximate the inversion by an L-order matrix polynomial. A set of
low-complexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel
(PEACH) estimators, are introduced. The coefficients of the polynomials are
optimized to yield small mean square error (MSE). We show numerically that
near-optimal performance is achieved with low polynomial orders. In practice,
the order L can be selected to balance between complexity and MSE.
Interestingly, pilot contamination is beneficial to the PEACH estimators in the
sense that smaller L can be used to achieve near-optimal MSEs.Comment: Published at IEEE International Symposium on Personal, Indoor and
Mobile Radio Communications (PIMRC 2013), 8-11 September 2013, 6 pages, 4
figures, 1 tabl
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