6 research outputs found
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
FDD Massive MIMO Based on Efficient Downlink Channel Reconstruction
Massive multiple-input multiple-output (MIMO) systems deploying a large
number of antennas at the base station considerably increase the spectrum
efficiency by serving multiple users simultaneously without causing severe
interference. However, the advantage relies on the availability of the downlink
channel state information (CSI) of multiple users, which is still a challenge
in frequency-division-duplex transmission systems. This paper aims to solve
this problem by developing a full transceiver framework that includes downlink
channel training (or estimation), CSI feedback, and channel reconstruction
schemes. Our framework provides accurate reconstruction results for multiple
users with small amounts of training and feedback overhead. Specifically, we
first develop an enhanced Newtonized orthogonal matching pursuit (eNOMP)
algorithm to extract the frequency-independent parameters (i.e., downtilts,
azimuths, and delays) from the uplink. Then, by leveraging the information from
these frequency-independent parameters, we develop an efficient downlink
training scheme to estimate the downlink channel gains for multiple users. This
training scheme offers an acceptable estimation error rate of the gains with a
limited pilot amount. Numerical results verify the precision of the eNOMP
algorithm and demonstrate that the sum-rate performance of the system using the
reconstructed downlink channel can approach that of the system using perfect
CSI
Massive MIMO Transmission for LEO Satellite Communications
Low earth orbit (LEO) satellite communications are expected to be
incorporated in future wireless networks, in particular 5G and beyond networks,
to provide global wireless access with enhanced data rates. Massive MIMO
techniques, though widely used in terrestrial communication systems, have not
been applied to LEO satellite communication systems. In this paper, we propose
a massive MIMO transmission scheme with full frequency reuse (FFR) for LEO
satellite communication systems and exploit statistical channel state
information (sCSI) to address the difficulty of obtaining instantaneous CSI
(iCSI) at the transmitter. We first establish the massive MIMO channel model
for LEO satellite communications and simplify the transmission designs via
performing Doppler and delay compensations at user terminals (UTs). Then, we
develop the low-complexity sCSI based downlink (DL) precoder and uplink (UL)
receiver in closed-form, aiming to maximize the average
signal-to-leakage-plus-noise ratio (ASLNR) and the average
signal-to-interference-plus-noise ratio (ASINR), respectively. It is shown that
the DL ASLNRs and UL ASINRs of all UTs reach their upper bounds under some
channel condition. Motivated by this, we propose a space angle based user
grouping (SAUG) algorithm to schedule the served UTs into different groups,
where each group of UTs use the same time and frequency resource. The proposed
algorithm is asymptotically optimal in the sense that the lower and upper
bounds of the achievable rate coincide when the number of satellite antennas or
UT groups is sufficiently large. Numerical results demonstrate that the
proposed massive MIMO transmission scheme with FFR significantly enhances the
data rate of LEO satellite communication systems. Notably, the proposed sCSI
based precoder and receiver achieve the similar performance with the iCSI based
ones that are often infeasible in practice.Comment: 31 pages, 4 figure
FDD Massive MIMO Based on Efficient Downlink Channel Reconstruction
This paper focuses on frequency division duplex (FDD) massive multiple-input multiple-output
(MIMO) systems and proposes a transceiver design that fully exploits the downlink spatial multiplexing
gain with only a small amount of overhead. The bottleneck lies in the acquisition of downlink channel
state information (CSI), which occurs when large scale antenna array is employed in FDD transmission
systems. Fortunately, the spatial reciprocity between uplink and downlink inspires us to reconstruct
the downlink channel based on the frequency-independent parameters (downtilts, azimuths and delays)
that can be derived in the uplink. We first extract these parameters through an enhanced Newtonized
orthogonal matching pursuit (e-NOMP) algorithm which is proposed in this paper to fit the massive
MIMO orthogonal frequency division multiplexing (OFDM) system. After formulating the requirement
to achieve an acceptable estimation error rate, we propose a low-cost downlink training scheme to
estimate the downlink gains of each user channel. This scheme saves the training time resource by
introducing a predefined spatial angle grid which corresponds to a beam set and by minimizing the
number of selected beams which is equal to the number of OFDM symbols used for downlink training.
Having obtained the reconstructed multiuser channel, the BS can maximize the spatial multiplexing gain
by serving all the users simultaneously without causing severe interference. Numerical results verify the
precision of the e-NOMP algorithm, and demonstrate that sum-rate performance of the reconstructionbased transceiver design approximates that of using perfect CSI