210 research outputs found
Downlink Performance of Superimposed Pilots in Massive MIMO systems
In this paper, we investigate the downlink throughput performance of a
massive multiple-input multiple-output (MIMO) system that employs superimposed
pilots for channel estimation. The component of downlink (DL) interference that
results from transmitting data alongside pilots in the uplink (UL) is shown to
decrease at a rate proportional to the square root of the number of antennas at
the BS. The normalized mean-squared error (NMSE) of the channel estimate is
compared with the Bayesian Cram\'{e}r-Rao lower bound that is derived for the
system, and the former is also shown to diminish with increasing number of
antennas at the base station (BS). Furthermore, we show that staggered pilots
are a particular case of superimposed pilots and offer the downlink throughput
of superimposed pilots while retaining the UL spectral and energy efficiency of
regular pilots. We also extend the framework for designing a hybrid system,
consisting of users that transmit either regular or superimposed pilots, to
minimize both the UL and DL interference. The improved NMSE and DL rates of the
channel estimator based on superimposed pilots are demonstrated by means of
simulations.Comment: 28 single-column pages, 6 figures, 1 table, Submitted to IEEE Trans.
Wireless Commun. in Aug 2017. Revised Submission in Feb. 201
Spectral and Energy Efficiency of Superimposed Pilots in Uplink Massive MIMO
Next generation wireless networks aim at providing substantial improvements
in spectral efficiency (SE) and energy efficiency (EE). Massive MIMO has been
proved to be a viable technology to achieve these goals by spatially
multiplexing several users using many base station (BS) antennas. A potential
limitation of Massive MIMO in multicell systems is pilot contamination, which
arises in the channel estimation process from the interference caused by
reusing pilots in neighboring cells. A standard method to reduce pilot
contamination, known as regular pilot (RP), is to adjust the length of pilot
sequences while transmitting data and pilot symbols disjointly. An alternative
method, called superimposed pilot (SP), sends a superposition of pilot and data
symbols. This allows to use longer pilots which, in turn, reduces pilot
contamination. We consider the uplink of a multicell Massive MIMO network using
maximum ratio combining detection and compare RP and SP in terms of SE and EE.
To this end, we derive rigorous closed-form achievable rates with SP under a
practical random BS deployment. We prove that the reduction of pilot
contamination with SP is outweighed by the additional coherent and non-coherent
interference. Numerical results show that when both methods are optimized, RP
achieves comparable SE and EE to SP in practical scenarios.Comment: 32 pages, 12 figures, 3 tables. Submitted in March 2017 to IEEE
Transactions on Wireless Communication
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
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