250 research outputs found
Massive MU-MIMO-OFDM Downlink with One-Bit DACs and Linear Precoding
Massive multiuser (MU) multiple-input multiple- output (MIMO) is foreseen to
be a key technology in future wireless communication systems. In this paper, we
analyze the downlink performance of an orthogonal frequency division
multiplexing (OFDM)-based massive MU-MIMO system in which the base station (BS)
is equipped with 1-bit digital-to-analog converters (DACs). Using Bussgang's
theorem, we characterize the performance achievable with linear precoders (such
as maximal-ratio transmission and zero forcing) in terms of bit error rate
(BER). Our analysis accounts for the possibility of oversampling the
time-domain transmit signal before the DACs. We further develop a lower bound
on the information-theoretic sum-rate throughput achievable with Gaussian
inputs.
Our results suggest that the performance achievable with 1-bit DACs in a
massive MU-MIMO-OFDM downlink are satisfactory provided that the number of BS
antennas is sufficiently large
A proposed technique for the Venus balloon telemetry and Doppler frequency recovery
A technique is proposed to accurately estimate the Doppler frequency and demodulate the digitally encoded telemetry signal that contains the measurements from balloon instruments. Since the data are prerecorded, one can take advantage of noncausal estimators that are both simpler and more computationally efficient than the usual closed-loop or real-time estimators for signal detection and carrier tracking. Algorithms for carrier frequency estimation subcarrier demodulation, bit and frame synchronization are described. A Viterbi decoder algorithm using a branch indexing technique has been devised to decode constraint length 6, rate 1/2 convolutional code that is being used by the balloon transmitter. These algorithms are memory efficient and can be implemented on microcomputer systems
On the Asymptotic Performance of Bit-Wise Decoders for Coded Modulation
Two decoder structures for coded modulation over the Gaussian and flat fading
channels are studied: the maximum likelihood symbol-wise decoder, and the
(suboptimal) bit-wise decoder based on the bit-interleaved coded modulation
paradigm. We consider a 16-ary quadrature amplitude constellation labeled by a
Gray labeling. It is shown that the asymptotic loss in terms of pairwise error
probability, for any two codewords caused by the bit-wise decoder, is bounded
by 1.25 dB. The analysis also shows that for the Gaussian channel the
asymptotic loss is zero for a wide range of linear codes, including all
rate-1/2 convolutional codes
Replacing the Soft FEC Limit Paradigm in the Design of Optical Communication Systems
The FEC limit paradigm is the prevalent practice for designing optical
communication systems to attain a certain bit-error rate (BER) without forward
error correction (FEC). This practice assumes that there is an FEC code that
will reduce the BER after decoding to the desired level. In this paper, we
challenge this practice and show that the concept of a channel-independent FEC
limit is invalid for soft-decision bit-wise decoding. It is shown that for low
code rates and high order modulation formats, the use of the soft FEC limit
paradigm can underestimate the spectral efficiencies by up to 20%. A better
predictor for the BER after decoding is the generalized mutual information,
which is shown to give consistent post-FEC BER predictions across different
channel conditions and modulation formats. Extensive optical full-field
simulations and experiments are carried out in both the linear and nonlinear
transmission regimes to confirm the theoretical analysis
A General Framework for Transmission with Transceiver Distortion and Some Applications
A general theoretical framework is presented for analyzing information
transmission over Gaussian channels with memoryless transceiver distortion,
which encompasses various nonlinear distortion models including transmit-side
clipping, receive-side analog-to-digital conversion, and others. The framework
is based on the so-called generalized mutual information (GMI), and the
analysis in particular benefits from the setup of Gaussian codebook ensemble
and nearest-neighbor decoding, for which it is established that the GMI takes a
general form analogous to the channel capacity of undistorted Gaussian
channels, with a reduced "effective" signal-to-noise ratio (SNR) that depends
on the nominal SNR and the distortion model. When applied to specific
distortion models, an array of results of engineering relevance is obtained.
For channels with transmit-side distortion only, it is shown that a
conventional approach, which treats the distorted signal as the sum of the
original signal part and a uncorrelated distortion part, achieves the GMI. For
channels with output quantization, closed-form expressions are obtained for the
effective SNR and the GMI, and related optimization problems are formulated and
solved for quantizer design. Finally, super-Nyquist sampling is analyzed within
the general framework, and it is shown that sampling beyond the Nyquist rate
increases the GMI for all SNR. For example, with a binary symmetric output
quantization, information rates exceeding one bit per channel use are
achievable by sampling the output at four times the Nyquist rate.Comment: 32 pages (including 4 figures, 5 tables, and auxiliary materials);
submitted to IEEE Transactions on Communication
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