887 research outputs found
Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks
Recent explorations of Deep Learning in the physical layer (PHY) of wireless
communication have shown the capabilities of Deep Neuron Networks in tasks like
channel coding, modulation, and parametric estimation. However, it is unclear
if Deep Neuron Networks could also learn the advanced waveforms of current and
next-generation wireless networks, and potentially create new ones. In this
paper, a Deep Complex Convolutional Network (DCCN) without explicit Discrete
Fourier Transform (DFT) is developed as an Orthogonal Frequency-Division
Multiplexing (OFDM) receiver. Compared to existing deep neuron network
receivers composed of fully-connected layers followed by non-linear
activations, the developed DCCN not only contains convolutional layers but is
also almost (and could be fully) linear. Moreover, the developed DCCN not only
learns to convert OFDM waveform with Quadrature Amplitude Modulation (QAM) into
bits under noisy and Rayleigh channels, but also outperforms expert OFDM
receiver based on Linear Minimum Mean Square Error channel estimator with prior
channel knowledge in the low to middle Signal-to-Noise Ratios of Rayleigh
channels. It shows that linear Deep Neuron Networks could learn transformations
in signal processing, thus master advanced waveforms and wireless channels.Comment: 12 pages, 20 figures, manuscrip
Complex support vector machines regression for robust channel estimation in LTE downlink system
In this paper, the problem of channel estimation for LTE Downlink system in
the environment of high mobility presenting non-Gaussian impulse noise
interfering with reference signals is faced. The estimation of the frequency
selective time varying multipath fading channel is performed by using a channel
estimator based on a nonlinear complex Support Vector Machine Regression (SVR)
which is applied to Long Term Evolution (LTE) downlink. The estimation
algorithm makes use of the pilot signals to estimate the total frequency
response of the highly selective fading multipath channel. Thus, the algorithm
maps trained data into a high dimensional feature space and uses the structural
risk minimization principle to carry out the regression estimation for the
frequency response function of the fading channel. The obtained results show
the effectiveness of the proposed method which has better performance than the
conventional Least Squares (LS) and Decision Feedback methods to track the
variations of the fading multipath channel.Comment: 13 pages Vol.4, IJCNC (2012) No.1, January 2012. arXiv admin note:
substantial text overlap with arXiv:1109.089
Parallel-Interference-Cancellation-Assisted Decision-Directed Channel Estimation for OFDM Systems using Multiple Transmit Antennas
The number of transmit antennas that can be employed in the context of least-squares (LS) channel estimation contrived for orthogonal frequency division multiplexing (OFDM) systems employing multiple transmit antennas is limited by the ratio of the number of subcarriers and the number of significant channel impulse response (CIR)-related taps. In order to allow for more complex scenarios in terms of the number of transmit antennas and users supported, CIR-related tap prediction-filtering-based parallel interference cancellation (PIC)-assisted decision-directed channel estimation (DDCE) is investigated. New explicit expressions are derived for the estimatorâs mean-square error (MSE), and a new iterative procedure is devised for the offline optimization of the CIR-related tap predictor coefficients. These new expressions are capable of accounting for the estimatorâs novel recursive structure. In the context of our performance results, it is demonstrated, for example, that the estimator is capable of supporting L = 16 transmit antennas, when assuming K = 512 subcarriers and K0 = 64 significant CIR taps, while LS-optimized DDCE would be limited to employing L = 8 transmit antennas. Index TermsâDecision-directed channel estimation (DDCE), multiple transmit antennas, orthogonal frequency division multiplexing (OFDM), parallel interference cancellation (PIC)
Sparse mmWave OFDM Channel Estimation Using Compressed Sensing in OFDM Systems
This paper proposes and analyzes a mmWave sparse channel estimation technique
for OFDM systems that uses the Orthogonal Matching Pursuit (OMP) algorithm.
This greedy algorithm retrieves one additional multipath component (MPC) per
iteration until a stop condition is met. We obtain an analytical approximation
for the OMP estimation error variance that grows with the number of retrieved
MPCs (iterations). The OMP channel estimator error variance outperforms a
classic maximum-likelihood (ML) non-sparse channel estimator by a factor of
approximately where is the number of retrieved MPCs
(iterations) and the number of taps of the Discrete Equivalent Channel.
When the MPC amplitude distribution is heavy-tailed, the channel power is
concentrated in a subset of dominant MPCs. In this case OMP performs fewer
iterations as it retrieves only these dominant large MPCs. Hence for this MPC
amplitude distribution the estimation error advantage of OMP over ML is
improved. In particular, for channels with MPCs that have
lognormally-distributed amplitudes, the OMP estimator recovers approximately
5-15 dominant MPCs in typical mmWave channels, with 15-45 weak MPCs that remain
undetected.Comment: Preprint submitted to IEEE ICC 201
Performance analysis and optimization of DCT-based multicarrier system on frequency-selective fading channels
Regarded as one of the most promising transmission techniques for future wireless communications, the discrete cosine transform (DCT) based multicarrier modulation (MCM) system employs cosine basis as orthogonal functions for real-modulated symbols multiplexing, by which the minimum orthogonal frequency spacing can be reduced by half compared to discrete Fourier transform (DFT) based one. With a time-reversed pre-filter employed at the front of the receiver, interference-free one-tap equalization is achievable for the DCT-based systems. However, due to the correlated pre-filtering operation in time domain, the signal-to-noise ratio (SNR) is enhanced as a result at the output. This leads to reformulated detection criterion to compensate for such filtering effect, rendering minimum-mean-square-error (MMSE) and maximum likelihood (ML) detections applicable to the DCT-based multicarrier system. In this paper, following on the pre-filtering based DCT-MCM model that build in the literature work, we extend the overall system by considering both transceiver perfections and imperfections, where frequency offset, time offset and insufficient guard sequence are included. In the presence of those imperfection errors, the DCT-MCM systems are analysed in terms of desired signal power, inter-carrier interference (ICI) and inter-symbol interference (ISI). Thereafter, new detection algorithms based on zero forcing (ZF) iterative results are proposed to mitigate the imperfection effect. Numerical results show that the theoretical analysis match the simulation results, and the proposed iterative detection algorithms are able to improve the overall system performance significantly
Orthogonal Chirp Division Multiplexing
Chirp waveform plays a significant role in radar and communication systems
for its ability of pulse compression and spread spectrum. This paper presents a
principle of orthogonally multiplexing a bank of linear chirp waveforms within
the same bandwidth. The amplitude and phase of the chirps are modulated for
information communication. As Fourier trans-form is the basis for orthogonal
frequency division multiplexing (OFDM), Fresnel transform underlies the
proposed orthogonal chirp division multiplexing (OCDM). Digital implementa-tion
of the OCDM system using discrete Fresnel transform is proposed. Based on the
con-volution theorem of the Fresnel transform, the transmission of the OCDM
signal is analyzed under the linear time-invariant or quasi-static channel with
additive noise, which can gener-alize typical linear transmission channels.
Based on the eigen-decomposition of Fresnel transform, efficient digital signal
processing algorithm is proposed for compensating chan-nel dispersion by linear
single- tap equalizers. The implementation details of the OCDM system is
discussed with emphasis on its compatibility to the OFDM system. Finally,
simula-tion are provided to validate the feasibility of the proposed OCDM under
wireless channels. It is shown that the OCDM system is able to utilize the
multipath diversity and outperforms the OFDM system under the multipath fading
channels.Comment: 27 pages, 10 figure
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
Near-Instantaneously Adaptive HSDPA-Style OFDM Versus MC-CDMA Transceivers for WIFI, WIMAX, and Next-Generation Cellular Systems
Burts-by-burst (BbB) adaptive high-speed downlink packet access (HSDPA) style multicarrier systems are reviewed, identifying their most critical design aspects. These systems exhibit numerous attractive features, rendering them eminently eligible for employment in next-generation wireless systems. It is argued that BbB-adaptive or symbol-by-symbol adaptive orthogonal frequency division multiplex (OFDM) modems counteract the near instantaneous channel quality variations and hence attain an increased throughput or robustness in comparison to their fixed-mode counterparts. Although they act quite differently, various diversity techniques, such as Rake receivers and space-time block coding (STBC) are also capable of mitigating the channel quality variations in their effort to reduce the bit error ratio (BER), provided that the individual antenna elements experience independent fading. By contrast, in the presence of correlated fading imposed by shadowing or time-variant multiuser interference, the benefits of space-time coding erode and it is unrealistic to expect that a fixed-mode space-time coded system remains capable of maintaining a near-constant BER
Faster-than-Nyquist Non-Orthogonal Frequency-Division Multiplexing for Visible Light Communications
In this paper, we propose a faster-than-Nyquist (FTN) non-orthogonal
frequency-division multiplexing (NOFDM) scheme for visible light communications
(VLC) where the multiplexing/demultiplexing employs the inverse fractional
cosine transform (IFrCT)/FrCT. Different to the common fractional Fourier
transform-based NOFDM (FrFT-NOFDM) signal, FrCT-based NOFDM (FrCT-NOFDM) signal
is real-valued which can be directly applied to the VLC systems without the
expensive upconversion. Thus, FrCT-NOFDM is more suitable for the
cost-sensitive VLC systems. Meanwhile, under the same transmission rate,
FrCT-NOFDM signal occupies smaller bandwidth compared to OFDM signal. When the
bandwidth compression factor is set to , bandwidth saving
can be obtained. Therefore, FrCT-NOFDM has higher spectral efficiency and
suffers less high-frequency distortion compared to OFDM, which benefits the
bandwidth-limited VLC systems. As the simulation results show, bit error rate
(BER) performance of FrCT-NOFDM with of or is better than
that of OFDM. Moreover, FrCT-NOFDM has a superior security performance. In
conclusion, FrCT-NOFDM shows great potential for application in the future VLC
systems.Comment: Under review of Journal of Lightwave Technolog
Channel Estimation for Orthogonal Time Frequency Space (OTFS) Massive MIMO
Orthogonal time frequency space (OTFS) modulation outperforms orthogonal
frequency division multiplexing (OFDM) in high-mobility scenarios. One
challenge for OTFS massive MIMO is downlink channel estimation due to the large
number of base station antennas. In this paper, we propose a 3D structured
orthogonal matching pursuit algorithm based channel estimation technique to
solve this problem. First, we show that the OTFS MIMO channel exhibits 3D
structured sparsity: normal sparsity along the delay dimension, block sparsity
along the Doppler dimension, and burst sparsity along the angle dimension.
Based on the 3D structured channel sparsity, we then formulate the downlink
channel estimation problem as a sparse signal recovery problem. Simulation
results show that the proposed algorithm can achieve accurate channel state
information with low pilot overhead
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