4,841 research outputs found
Multipair Full-Duplex Relaying with Massive Arrays and Linear Processing
We consider a multipair decode-and-forward relay channel, where multiple
sources transmit simultaneously their signals to multiple destinations with the
help of a full-duplex relay station. We assume that the relay station is
equipped with massive arrays, while all sources and destinations have a single
antenna. The relay station uses channel estimates obtained from received pilots
and zero-forcing (ZF) or maximum-ratio combining/maximum-ratio transmission
(MRC/MRT) to process the signals. To reduce significantly the loop interference
effect, we propose two techniques: i) using a massive receive antenna array; or
ii) using a massive transmit antenna array together with very low transmit
power at the relay station. We derive an exact achievable rate in closed-form
for MRC/MRT processing and an analytical approximation of the achievable rate
for ZF processing. This approximation is very tight, especially for large
number of relay station antennas. These closed-form expressions enable us to
determine the regions where the full-duplex mode outperforms the half-duplex
mode, as well as, to design an optimal power allocation scheme. This optimal
power allocation scheme aims to maximize the energy efficiency for a given sum
spectral efficiency and under peak power constraints at the relay station and
sources. Numerical results verify the effectiveness of the optimal power
allocation scheme. Furthermore, we show that, by doubling the number of
transmit/receive antennas at the relay station, the transmit power of each
source and of the relay station can be reduced by 1.5dB if the pilot power is
equal to the signal power, and by 3dB if the pilot power is kept fixed, while
maintaining a given quality-of-service
Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling
We consider the problem of separating speech sources captured by multiple
spatially separated devices, each of which has multiple microphones and samples
its signals at a slightly different rate. Most asynchronous array processing
methods rely on sample rate offset estimation and resampling, but these offsets
can be difficult to estimate if the sources or microphones are moving. We
propose a source separation method that does not require offset estimation or
signal resampling. Instead, we divide the distributed array into several
synchronous subarrays. All arrays are used jointly to estimate the time-varying
signal statistics, and those statistics are used to design separate
time-varying spatial filters in each array. We demonstrate the method for
speech mixtures recorded on both stationary and moving microphone arrays.Comment: To appear at the International Workshop on Acoustic Signal
Enhancement (IWAENC 2018
Massive MIMO Full-Duplex Relaying with Optimal Power Allocation for Independent Multipairs
With the help of an in-band full-duplex relay station, it is possible to
simultaneously transmit and receive signals from multiple users. The
performance of such system can be greatly increased when the relay station is
equipped with a large number of antennas on both transmitter and receiver
sides. In this paper, we exploit the use of massive arrays to effectively
suppress the loopback interference (LI) of a decode-and-forward relay (DF) and
evaluate the performance of the end-to-end (e2e) transmission. This paper
assumes imperfect channel state information is available at the relay and
designs a minimum mean-square error (MMSE) filter to mitigate the interference.
Subsequently, we adopt zero-forcing (ZF) filters for both detection and
beamforming. The performance of such system is evaluated in terms of bit error
rate (BER) at both relay and destinations, and an optimal choice for the
transmission power at the relay is shown. We then propose a complexity
efficient optimal power allocation (OPA) algorithm that, using the channel
statistics, computes the minimum power that satisfies the rate constraints of
each pair. The results obtained via simulation show that when both MMSE
filtering and OPA method are used, better values for the energy efficiency are
attained.Comment: Accepted to the 16th IEEE International Workshop on Signal Processing
Advances in Wireless Communications - SPAWC, Stockholm, Sweden 201
Massive MIMO Systems with Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits
The use of large-scale antenna arrays can bring substantial improvements in
energy and/or spectral efficiency to wireless systems due to the greatly
improved spatial resolution and array gain. Recent works in the field of
massive multiple-input multiple-output (MIMO) show that the user channels
decorrelate when the number of antennas at the base stations (BSs) increases,
thus strong signal gains are achievable with little inter-user interference.
Since these results rely on asymptotics, it is important to investigate whether
the conventional system models are reasonable in this asymptotic regime. This
paper considers a new system model that incorporates general transceiver
hardware impairments at both the BSs (equipped with large antenna arrays) and
the single-antenna user equipments (UEs). As opposed to the conventional case
of ideal hardware, we show that hardware impairments create finite ceilings on
the channel estimation accuracy and on the downlink/uplink capacity of each UE.
Surprisingly, the capacity is mainly limited by the hardware at the UE, while
the impact of impairments in the large-scale arrays vanishes asymptotically and
inter-user interference (in particular, pilot contamination) becomes
negligible. Furthermore, we prove that the huge degrees of freedom offered by
massive MIMO can be used to reduce the transmit power and/or to tolerate larger
hardware impairments, which allows for the use of inexpensive and
energy-efficient antenna elements.Comment: To appear in IEEE Transactions on Information Theory, 28 pages, 15
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/massive-MIMO-hardware-impairment
Deterministic Cramer-Rao bound for strictly non-circular sources and analytical analysis of the achievable gains
Recently, several high-resolution parameter estimation algorithms have been
developed to exploit the structure of strictly second-order (SO) non-circular
(NC) signals. They achieve a higher estimation accuracy and can resolve up to
twice as many signal sources compared to the traditional methods for arbitrary
signals. In this paper, as a benchmark for these NC methods, we derive the
closed-form deterministic R-D NC Cramer-Rao bound (NC CRB) for the
multi-dimensional parameter estimation of strictly non-circular (rectilinear)
signal sources. Assuming a separable centro-symmetric R-D array, we show that
in some special cases, the deterministic R-D NC CRB reduces to the existing
deterministic R-D CRB for arbitrary signals. This suggests that no gain from
strictly non-circular sources (NC gain) can be achieved in these cases. For
more general scenarios, finding an analytical expression of the NC gain for an
arbitrary number of sources is very challenging. Thus, in this paper, we
simplify the derived NC CRB and the existing CRB for the special case of two
closely-spaced strictly non-circular sources captured by a uniform linear array
(ULA). Subsequently, we use these simplified CRB expressions to analytically
compute the maximum achievable asymptotic NC gain for the considered two source
case. The resulting expression only depends on the various physical parameters
and we find the conditions that provide the largest NC gain for two sources.
Our analysis is supported by extensive simulation results.Comment: submitted to IEEE Transactions on Signal Processing, 13 pages, 4
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