1,739 research outputs found
Cell-Free Massive MIMO versus Small Cells
A Cell-Free Massive MIMO (multiple-input multiple-output) system comprises a
very large number of distributed access points (APs)which simultaneously serve
a much smaller number of users over the same time/frequency resources based on
directly measured channel characteristics. The APs and users have only one
antenna each. The APs acquire channel state information through time-division
duplex operation and the reception of uplink pilot signals transmitted by the
users. The APs perform multiplexing/de-multiplexing through conjugate
beamforming on the downlink and matched filtering on the uplink. Closed-form
expressions for individual user uplink and downlink throughputs lead to max-min
power control algorithms. Max-min power control ensures uniformly good service
throughout the area of coverage. A pilot assignment algorithm helps to mitigate
the effects of pilot contamination, but power control is far more important in
that regard.
Cell-Free Massive MIMO has considerably improved performance with respect to
a conventional small-cell scheme, whereby each user is served by a dedicated
AP, in terms of both 95%-likely per-user throughput and immunity to shadow
fading spatial correlation. Under uncorrelated shadow fading conditions, the
cell-free scheme provides nearly 5-fold improvement in 95%-likely per-user
throughput over the small-cell scheme, and 10-fold improvement when shadow
fading is correlated.Comment: EEE Transactions on Wireless Communications, accepted for publicatio
Linear Precoding with Low-Resolution DACs for Massive MU-MIMO-OFDM Downlink
We consider the downlink of a massive multiuser (MU) multiple-input
multiple-output (MIMO) system in which the base station (BS) is equipped with
low-resolution digital-to-analog converters (DACs). In contrast to most
existing results, we assume that the system operates over a frequency-selective
wideband channel and uses orthogonal frequency division multiplexing (OFDM) to
simplify equalization at the user equipments (UEs). Furthermore, we consider
the practically relevant case of oversampling DACs. We theoretically analyze
the uncoded bit error rate (BER) performance with linear precoders (e.g., zero
forcing) and quadrature phase-shift keying using Bussgang's theorem. We also
develop a lower bound on the information-theoretic sum-rate throughput
achievable with Gaussian inputs, which can be evaluated in closed form for the
case of 1-bit DACs. For the case of multi-bit DACs, we derive approximate, yet
accurate, expressions for the distortion caused by low-precision DACs, which
can be used to establish lower bounds on the corresponding sum-rate throughput.
Our results demonstrate that, for a massive MU-MIMO-OFDM system with a
128-antenna BS serving 16 UEs, only 3--4 DAC bits are required to achieve an
uncoded BER of 10^-4 with a negligible performance loss compared to the
infinite-resolution case at the cost of additional out-of-band emissions.
Furthermore, our results highlight the importance of taking into account the
inherent spatial and temporal correlations caused by low-precision DACs
Boosting Fronthaul Capacity: Global Optimization of Power Sharing for Centralized Radio Access Network
The limited fronthaul capacity imposes a challenge on the uplink of
centralized radio access network (C-RAN). We propose to boost the fronthaul
capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by
globally optimizing the power sharing between channel estimation and data
transmission both for the user devices (UDs) and the remote radio units (RRUs).
Intuitively, allocating more power to the channel estimation will result in
more accurate channel estimates, which increases the achievable throughput.
However, increasing the power allocated to the pilot training will reduce the
power assigned to data transmission, which reduces the achievable throughput.
In order to optimize the powers allocated to the pilot training and to the data
transmission of both the UDs and the RRUs, we assign an individual power
sharing factor to each of them and derive an asymptotic closed-form expression
of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN
consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU)
links. We then exploit the C-RAN architecture's central computing and control
capability for jointly optimizing the UDs' power sharing factors and the RRUs'
power sharing factors aiming for maximizing the fronthaul capacity. Our
simulation results show that the fronthaul capacity is significantly boosted by
the proposed global optimization of the power allocation between channel
estimation and data transmission both for the UDs and for their host RRUs. As a
specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs
deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing
factors improves 33\% compared with the one attained without optimizing power
sharing factors
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