1,439 research outputs found
Joint maximum-likelihood channel estimation and signal detection for SIMO channels
In wireless communication systems, channel state information is often assumed to be available at the receiver. Traditionally, a training sequence is used to obtain the estimate of the channel. Alternatively, the channel can be identified using known properties of the transmitted signal. However, the computational effort required to find the joint ML solution to the symbol detection and channel estimation problem increases exponentially with the dimension of the problem. To significantly reduce this computational effort, we formulate the aforementioned problem in a way that makes it possible to solve it via the use of sphere decoding, an algorithm that has polynomial expected complexity. We also provide simulation results and a complexity discussion
Low-complexity blind maximum-likelihood detection for SIMO systems with general constellations
The demand for high data rate reliable communications poses great challenges to the next generation wireless systems in highly dynamic mobile environments. In this paper, we investigate the joint maximum-likelihood (ML) channel estimation and signal detection problem for single-input multiple-output (SIMO) wireless systems with general modulation constellations and propose an efficient sequential decoder for finding the exact joint ML solution. Unlike other known methods, the new decoder can even efficiently find the joint ML solution under high spectral efficiency non-constant- modulus modulation constellations. In particular, the new algorithm does not need such preprocessing steps as Cholesky or QR decomposition in the traditional sphere decoders for joint ML channel estimation and data detection. The elimination of such preprocessing not only reduces the number of floating point computations, but also will potentially lead to smaller size and power consumption in VLSI implementations while providing better numerical stability
Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems
A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimization of the channel and data estimation is decomposed into an iterative optimization loop. An efficient global optimization algorithm termed as the repeated weighted boosting aided search is employed first to identify the unknown SIMO channel model, and then the Viterbi algorithm is used for the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used for demonstrating the efficiency of this joint ML optimization scheme designed for blind adaptive SIMO systems
Receiver Algorithm based on Differential Signaling for SIMO Phase Noise Channels with Common and Separate Oscillator Configurations
In this paper, a receiver algorithm consisting of differential transmission
and a two-stage detection for a single-input multiple-output (SIMO) phase-noise
channels is studied. Specifically, the phases of the QAM modulated data symbols
are manipulated before transmission in order to make them more immune to the
random rotational effects of phase noise. At the receiver, a two-stage detector
is implemented, which first detects the amplitude of the transmitted symbols
from a nonlinear combination of the received signal amplitudes. Then in the
second stage, the detector performs phase detection. The studied signaling
method does not require transmission of any known symbols that act as pilots.
Furthermore, no phase noise estimator (or a tracker) is needed at the receiver
to compensate the effect of phase noise. This considerably reduces the
complexity of the receiver structure. Moreover, it is observed that the studied
algorithm can be used for the setups where a common local oscillator or
separate independent oscillators drive the radio-frequency circuitries
connected to each antenna. Due to the differential encoding/decoding of the
phase, weighted averaging can be employed at a multi-antenna receiver, allowing
for phase noise suppression to leverage the large number of antennas. Hence, we
observe that the performance improves by increasing the number of antennas,
especially in the separate oscillator case. Further increasing the number of
receive antennas results in a performance error floor, which is a function of
the quality of the oscillator at the transmitter.Comment: IEEE GLOBECOM 201
Efficient joint maximum-likelihood channel estimation and signal detection
In wireless communication systems, channel state information is often assumed to be available at the receiver. Traditionally, a training sequence is used to obtain the estimate of the channel. Alternatively, the channel can be identified using known properties of the transmitted signal. However, the computational effort required to find the joint ML solution to the symbol detection and channel estimation problem increases exponentially with the dimension of the problem. To significantly reduce this computational effort, we formulate the joint ML estimation and detection as an integer least-squares problem, and show that for a wide range of signal-to-noise ratios (SNR) and problem dimensions it can be solved via sphere decoding with expected complexity comparable to the complexity of heuristic
techniques
Efficient Optimal Joint Channel Estimation and Data Detection for Massive MIMO Systems
In this paper, we propose an efficient optimal joint channel estimation and
data detection algorithm for massive MIMO wireless systems. Our algorithm is
optimal in terms of the generalized likelihood ratio test (GLRT). For massive
MIMO systems, we show that the expected complexity of our algorithm grows
polynomially in the channel coherence time. Simulation results demonstrate
significant performance gains of our algorithm compared with suboptimal
non-coherent detection algorithms. To the best of our knowledge, this is the
first algorithm which efficiently achieves GLRT-optimal non-coherent detections
for massive MIMO systems with general constellations.Comment: 5 pages, 4 figures, Conferenc
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