503 research outputs found
A Simple Capacity Lower Bound for Communication with Superimposed Pilots
We present a novel closed-form lower bound on the Gaussian-input mutual
information for noncoherent communication (i.e., in which neither transmitter
nor receiver are cognizant of the fading state) over a frequency-flat fading
channel with additive noise. Our bound yields positive (non-trivial) values
even in the most challenging case of zero-mean fast fading, a regime in which
the conventional approach of orthogonal time-multiplexed pilots is unavailing
and for which, to the best of the author's knowledge, no simple analytical
bound is known. Its derivation relies on endowing the transmit signal with a
non-zero mean, which can be interpreted as a pilot symbol that is additively
superimposed onto the information-bearing Gaussian signal. The optimal fraction
of transmit power that one should dedicate to this pilot is computed in closed
form and shown to tend to one half at low SNR and to a limit above at high SNR. We further show how one can refine the bound for
the general case of non-zero mean fading. Finally, we state an extension of our
bound to the MIMO setting and apply it to compare superimposed vs. orthogonal
pilots on the SISO Rayleigh block fading channel.Comment: 5 pages, submitted to ISWCS 201
Breaking the Interference Barrier in Dense Wireless Networks with Interference Alignment
A fundamental problem arising in dense wireless networks is the high
co-channel interference. Interference alignment (IA) was recently proposed as
an effective way to combat interference in wireless networks. The concept of
IA, though, is originated by the capacity study of interference channels and as
such, its performance is mainly gauged under ideal assumptions, such as
instantaneous and perfect channel state information (CSI) at all nodes, and
homogeneous signal-to-noise ratio (SNR) users, i.e., each user has the same
average SNR. Consequently, the performance of IA under realistic conditions has
not been completely investigated yet. In this paper, we aim at filling this gap
by providing a performance assessment of spatial IA in practical systems.
Specifically, we derive a closed-form expression for the IA average sum-rate
when CSI is acquired through training and users have heterogeneous SNR. A main
insight from our analysis is that IA can indeed provide significant spectral
efficiency gains over traditional approaches in a wide range of dense network
scenarios. To demonstrate this, we consider the examples of linear, grid and
random network topologies
A Data-Aided Channel Estimation Scheme for Decoupled Systems in Heterogeneous Networks
Uplink/downlink (UL/DL) decoupling promises more flexible cell association
and higher throughput in heterogeneous networks (HetNets), however, it hampers
the acquisition of DL channel state information (CSI) in time-division-duplex
(TDD) systems due to different base stations (BSs) connected in UL/DL. In this
paper, we propose a novel data-aided (DA) channel estimation scheme to address
this problem by utilizing decoded UL data to exploit CSI from received UL data
signal in decoupled HetNets where a massive multiple-input multiple-output BS
and dense small cell BSs are deployed. We analytically estimate BER performance
of UL decoded data, which are used to derive an approximated normalized mean
square error (NMSE) expression of the DA minimum mean square error (MMSE)
estimator. Compared with the conventional least square (LS) and MMSE, it is
shown that NMSE performances of all estimators are determined by their
signal-to-noise ratio (SNR)-like terms and there is an increment consisting of
UL data power, UL data length and BER values in the SNR-like term of DA method,
which suggests DA method outperforms the conventional ones in any scenarios.
Higher UL data power, longer UL data length and better BER performance lead to
more accurate estimated channels with DA method. Numerical results verify that
the analytical BER and NMSE results are close to the simulated ones and a
remarkable gain in both NMSE and DL rate can be achieved by DA method in
multiple scenarios with different modulations
Two-tier channel estimation aided near-capacity MIMO transceivers relying on norm-based joint transmit and receive antenna selection
We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains, while dispensing with the idealised simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximumlikelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process
Performance Analysis of Active Large Intelligent Surfaces (LISs): Uplink Spectral Efficiency and Pilot Training
Large intelligent surfaces (LISs) constitute a new and promising wireless
communication paradigm that relies on the integration of a massive number of
antenna elements over the entire surfaces of man-made structures. The LIS
concept provides many advantages, such as the capability to provide reliable
and space-intensive communications by effectively establishing line-of-sight
(LOS) channels. In this paper, the system spectral efficiency (SSE) of an
uplink LIS system is asymptotically analyzed under a practical LIS environment
with a well-defined uplink frame structure. In order to verify the impact on
the SSE of pilot contamination, the SSE of a multi-LIS system is asymptotically
studied and a theoretical bound on its performance is derived. Given this
performance bound, an optimal pilot training length for multi-LIS systems
subjected to pilot contamination is characterized and, subsequently, the
performance-maximizing number of devices that the LIS system must service is
derived. Simulation results show that the derived analyses are in close
agreement with the exact mutual information in presence of a large number of
antennas, and the achievable SSE is limited by the effect of pilot
contamination and intra/inter-LIS interference through the LOS path, even if
the LIS is equipped with an infinite number of antennas. Additionally, the SSE
obtained with the proposed pilot training length and number of scheduled
devices is shown to reach the one obtained via a brute-force search for the
optimal solution
Modulation Formats and Waveforms for the Physical Layer of 5G Wireless Networks: Who Will be the Heir of OFDM?
5G cellular communications promise to deliver the gigabit experience to
mobile users, with a capacity increase of up to three orders of magnitude with
respect to current LTE systems. There is widespread agreement that such an
ambitious goal will be realized through a combination of innovative techniques
involving different network layers. At the physical layer, the OFDM modulation
format, along with its multiple-access strategy OFDMA, is not taken for
granted, and several alternatives promising larger values of spectral
efficiency are being considered. This paper provides a review of some
modulation formats suited for 5G, enriched by a comparative analysis of their
performance in a cellular environment, and by a discussion on their
interactions with specific 5G ingredients. The interaction with a massive MIMO
system is also discussed by employing real channel measurements.Comment: to appear IEEE Signal Processing Magazine, special issue on Signal
Processing for the 5G Revolution, November 201
Uplink Achievable Rate in One-bit Quantized Massive MIMO with Superimposed Pilots
In this work, we consider a 1-bit quantized massive MIMO channel with
superimposed pilot (SP) scheme, dubbed QSP. With linear minimum mean square
error (LMMSE) channel estimator and maximum ratio combining (MRC) receiver at
the BS, we derive an approximate lower bound on the achievable rate. When
optimizing pilot and data powers, the optimal power allocation maximizing the
data rate is obtained in a closed-form solution. Although there is a
performance gap between the quantized and unquantized systems, it is shown that
this gap diminishes as the number of BS antennas is asymptotically large.
Moreover, we show that pilot removal from the received signal by using the
channel estimate doesn't result in a significant increase in information,
especially in the cases of low signal-to-noise ratio (SNR) and a large number
of users. We present some numerical results to corroborate our analytical
findings and insights are provided for further exploration of the quantized
systems with SP.Comment: two-column, single-spaced 15 pages, 7 figure
Dispensing with channel estimation: differentially modulated cooperative wireless communications
As a benefit of bypassing the potentially excessive complexity and yet inaccurate channel estimation, differentially encoded modulation in conjunction with low-complexity noncoherent detection constitutes a viable candidate for user-cooperative systems, where estimating all the links by the relays is unrealistic. In order to stimulate further research on differentially modulated cooperative systems, a number of fundamental challenges encountered in their practical implementations are addressed, including the time-variant-channel-induced performance erosion, flexible cooperative protocol designs, resource allocation as well as its high-spectral-efficiency transceiver design. Our investigations demonstrate the quantitative benefits of cooperative wireless networks both from a pure capacity perspective as well as from a practical system design perspective
Structured Compressive Sensing Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO
Massive MIMO is a promising technique for future 5G communications due to its
high spectrum and energy efficiency. To realize its potential performance gain,
accurate channel estimation is essential. However, due to massive number of
antennas at the base station (BS), the pilot overhead required by conventional
channel estimation schemes will be unaffordable, especially for frequency
division duplex (FDD) massive MIMO. To overcome this problem, we propose a
structured compressive sensing (SCS)-based spatio-temporal joint channel
estimation scheme to reduce the required pilot overhead, whereby the
spatio-temporal common sparsity of delay-domain MIMO channels is leveraged.
Particularly, we first propose the non-orthogonal pilots at the BS under the
framework of CS theory to reduce the required pilot overhead. Then, an adaptive
structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly
estimate channels associated with multiple OFDM symbols from the limited number
of pilots, whereby the spatio-temporal common sparsity of MIMO channels is
exploited to improve the channel estimation accuracy. Moreover, by exploiting
the temporal channel correlation, we propose a space-time adaptive pilot scheme
to further reduce the pilot overhead. Additionally, we discuss the proposed
channel estimation scheme in multi-cell scenario. Simulation results
demonstrate that the proposed scheme can accurately estimate channels with the
reduced pilot overhead, and it is capable of approaching the optimal oracle
least squares estimator.Comment: 16 pages; 12 figures;submitted to IEEE Trans. Communication
Joint Channel-and-Data Estimation for Large-MIMO Systems with Low-Precision ADCs
The use of low precision (e.g., 1-3 bits) analog-to-digital convenors (ADCs)
in very large multiple-input multiple-output (MIMO) systems is a technique to
reduce cost and power consumption. In this context, nevertheless, it has been
shown that the training duration is required to be {\em very large} just to
obtain an acceptable channel state information (CSI) at the receiver. A
possible solution to the quantized MIMO systems is joint channel-and-data (JCD)
estimation. This paper first develops an analytical framework for studying the
quantized MIMO system using JCD estimation. In particular, we use the
Bayes-optimal inference for the JCD estimation and realize this estimator
utilizing a recent technique based on approximate message passing. Large-system
analysis based on the replica method is then adopted to derive the asymptotic
performances of the JCD estimator. Results from simulations confirm our
theoretical findings and reveal that the JCD estimator can provide a
significant gain over conventional pilot-only schemes in the quantized MIMO
system.Comment: 7 pages, 4 figure
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