11 research outputs found
Joint Channel Estimation Algorithm via Weighted Homotopy for Massive MIMO OFDM System
Massive (or large-scale) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is widely acknowledged as a key technology for future communication. One main challenge to implement this system in practice is the high dimensional channel estimation, where the large number of channel matrix entries requires prohibitively high computational complexity. To solve this problem efficiently, a channel estimation approach using few number of pilots is necessary. In this paper, we propose a weighted Homotopy based channel estimation approach which utilizes the sparse nature in MIMO channels to achieve a decent channel estimation performance with much less pilot overhead. Moreover, inspired by the fact that MIMO channels are observed to have approximately common support in a neighborhood, an information exchange strategy based on the proposed approach is developed to further improve the estimation accuracy and reduce the required number of pilots through joint channel estimation. Compared with the traditional sparse channel estimation methods, the proposed approach can achieve more than 2dB gain in terms of mean square error (MSE) with the same number of pilots, or achieve the same performance with much less pilots
Channel Estimation for Massive MIMO Systems
Massive multiple input multiple output (MIMO) systems can significantly improve the channel
capacity by deploying multiple antennas at the transmitter and receiver. Massive MIMO
is considered as one of key technologies of the next generation of wireless communication
systems. However, with the increase of the number of antennas at the base station, a large
number of unknown channel parameters need to be dealt with, which makes the channel
estimation a challenging problem. Hence, the research on the channel estimation for massive
MIMO is of great importance to the development of the next generation of communication
systems. The wireless multipath channel exhibits sparse characteristics, but the traditional
channel estimation techniques do not make use of the sparsity. The channel estimation
based on compressive sensing (CS) can make full use of the channel sparsity, while use
fewer pilot symbols. In this work, CS channel estimation methods are proposed for massive
MIMO systems in complex environments operating in multipath channels with static and
time-varying parameters. Firstly, a CS channel estimation algorithm for massive MIMO
systems with Orthogonal Frequency Division Multiplexing (OFDM) is proposed. By exploiting
the spatially common sparsity in the virtual angular domain of the massive MIMO
channels, a dichotomous-coordinate-decent-joint-sparse-recovery (DCD-JSR) algorithm is
proposed. More specifically, by considering the channel is static over several OFDM symbols
and exhibits common sparsity in the virtual angular domain, the DCD-JSR algorithm can
jointly estimate multiple sparse channels with low computational complexity. The simulation
results have shown that, compared to existing channel estimation algorithms such as the
distributed-sparsity-adaptive-matching-pursuit (DSAMP) algorithm, the proposed DCD-JSR
algorithm has significantly lower computational complexity and better performance. Secondly, these results have been extended to the case of multipath channels with time-varying
parameters. This has been achieved by employing the basis expansion model to approximate
the time variation of the channel, thus the modified DCD-JSR algorithm can estimate the
channel in a massive MIMO OFDM system operating over frequency selective and highly
mobile wireless channels. Simulation results have shown that, compared to the DCD-JSR
algorithm designed for time-invariant channels, the modified DCD-JSR algorithm provides
significantly better estimation performance in fast time-varying channels
CSI-Free Geometric Symbol Detection via Semi-supervised Learning and Ensemble Learning
Symbol detection (SD) plays an important role in a digital communication system. However, most SD algorithms require channel state information (CSI), which is often difficult to estimate accurately. As a consequence, it is challenging for these SD algorithms to approach the performance of the maximum likelihood detection (MLD) algorithm. To address this issue, we employ both semi-supervised learning and ensemble learning to design a flexible parallelizable approach in this paper. First, we prove theoretically that the proposed algorithms can arbitrarily approach the performance of the MLD algorithm with perfect CSI. Second, to enable parallel implementation and also enhance design flexibility, we further propose a parallelizable approach for multi-output systems. Finally, comprehensive simulation results are provided to demonstrate the effectiveness and superiority of the designed algorithms. In particular, the proposed algorithms approach the performance of the MLD algorithm with perfect CSI, and outperform it when the CSI is imperfect. Interestingly, a detector constructed with received signals from only two receiving antennas (less than the size of the whole receiving antenna array) can also provide good detection performance
Compressive Sensing of Multiband Spectrum towards Real-World Wideband Applications.
PhD Theses.Spectrum scarcity is a major challenge in wireless communication systems with their
rapid evolutions towards more capacity and bandwidth. The fact that the real-world
spectrum, as a nite resource, is sparsely utilized in certain bands spurs the proposal
of spectrum sharing. In wideband scenarios, accurate real-time spectrum sensing, as an
enabler of spectrum sharing, can become ine cient as it naturally requires the sampling
rate of the analog-to-digital conversion to exceed the Nyquist rate, which is resourcecostly
and energy-consuming. Compressive sensing techniques have been applied in
wideband spectrum sensing to achieve sub-Nyquist-rate sampling of frequency sparse
signals to alleviate such burdens.
A major challenge of compressive spectrum sensing (CSS) is the complexity of the sparse
recovery algorithm. Greedy algorithms achieve sparse recovery with low complexity but
the required prior knowledge of the signal sparsity. A practical spectrum sparsity estimation
scheme is proposed. Furthermore, the dimension of the sparse recovery problem
is proposed to be reduced, which further reduces the complexity and achieves signal
denoising that promotes recovery delity. The robust detection of incumbent radio is
also a fundamental problem of CSS. To address the energy detection problem in CSS,
the spectrum statistics of the recovered signals are investigated and a practical threshold
adaption scheme for energy detection is proposed. Moreover, it is of particular interest to
seek the challenges and opportunities to implement real-world CSS for systems with large
bandwidth. Initial research on the practical issues towards the real-world realization of
wideband CSS system based on the multicoset sampler architecture is presented.
In all, this thesis provides insights into two critical challenges - low-complexity sparse
recovery and robust energy detection - in the general CSS context, while also looks
into some particular issues towards the real-world CSS implementation based on the
i
multicoset sampler