140 research outputs found
Multidimensional Index Modulation in Wireless Communications
In index modulation schemes, information bits are conveyed through indexing
of transmission entities such as antennas, subcarriers, times slots, precoders,
subarrays, and radio frequency (RF) mirrors. Index modulation schemes are
attractive for their advantages such as good performance, high rates, and
hardware simplicity. This paper focuses on index modulation schemes in which
multiple transmission entities, namely, {\em antennas}, {\em time slots}, and
{\em RF mirrors}, are indexed {\em simultaneously}. Recognizing that such
multidimensional index modulation schemes encourage sparsity in their transmit
signal vectors, we propose efficient signal detection schemes that use
compressive sensing based reconstruction algorithms. Results show that, for a
given rate, improved performance is achieved when the number of indexed
transmission entities is increased. We also explore indexing opportunities in
{\em load modulation}, which is a modulation scheme that offers power
efficiency and reduced RF hardware complexity advantages in multiantenna
systems. Results show that indexing space and time in load modulated
multiantenna systems can achieve improved performance
Massive Access in Media Modulation Based Massive Machine-Type Communications
The massive machine-type communications (mMTC) paradigm based on media
modulation in conjunction with massive MIMO base stations (BSs) is emerging as
a viable solution to support the massive connectivity for the future
Internet-of-Things, in which the inherent massive access at the BSs poses
significant challenges for device activity and data detection (DADD). This
paper considers the DADD problem for both uncoded and coded media modulation
based mMTC with a slotted access frame structure, where the device activity
remains unchanged within one frame. Specifically, due to the slotted access
frame structure and the adopted media modulated symbols, the access signals
exhibit a doubly structured sparsity in both the time domain and the modulation
domain. Inspired by this, a doubly structured approximate message passing
(DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we
derive the state evolution of the DS-AMP algorithm to theoretically
characterize its performance. As for the coded case, we develop a
bit-interleaved coded media modulation scheme and propose an iterative DS-AMP
(IDS-AMP) algorithm based on successive inference cancellation (SIC), where the
signal components associated with the detected active devices are successively
subtracted to improve the data decoding performance. In addition, the channel
estimation problem for media modulation based mMTC is discussed and an
efficient data-aided channel state information (CSI) update strategy is
developed to reduce the training overhead in block fading channels. Finally,
simulation results and computational complexity analysis verify the superiority
of the proposed algorithms in both uncoded and coded cases. Also, our results
verify the validity of the proposed data-aided CSI update strategy.Comment: Accepted by IEEE Transactions on Wireless Communications. The codes
and some other materials about this work may be available at
https://gaozhen16.github.i
Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication
The advent of the sixth-generation (6G) of wireless communications has given
rise to the necessity to connect vast quantities of heterogeneous wireless
devices, which requires advanced system capabilities far beyond existing
network architectures. In particular, such massive communication has been
recognized as a prime driver that can empower the 6G vision of future
ubiquitous connectivity, supporting Internet of Human-Machine-Things for which
massive access is critical. This paper surveys the most recent advances toward
massive access in both academic and industry communities, focusing primarily on
the promising compressive sensing-based grant-free massive access paradigm. We
first specify the limitations of existing random access schemes and reveal that
the practical implementation of massive communication relies on a dramatically
different random access paradigm from the current ones mainly designed for
human-centric communications. Then, a compressive sensing-based grant-free
massive access roadmap is presented, where the evolutions from single-antenna
to large-scale antenna array-based base stations, from single-station to
cooperative massive multiple-input multiple-output systems, and from unsourced
to sourced random access scenarios are detailed. Finally, we discuss the key
challenges and open issues to shed light on the potential future research
directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa
Sparsity Signal Detection for Indoor GSSK-VLC System
In this paper, the signal detection problem in indoor
visible light communication (VLC) system aided by generalized
space shift keying (GSSK) is modeled as a sparse signal reconstruction problem, which has lower computational complexity by
exploiting the sparse reconstruction algorithms in compressed
sensing (CS). In order to satisfy the measurement matrix property to perform sparse signal reconstruction, a preprocessing
approach of measurement matrix is proposed based on singular
value decomposition (SVD), which theoretically guarantees the
feasibility of utilizing CS based sparse signal detection method in
indoor GSSK-VLC system. Then, by adopting classical orthogonal matching pursuit (OMP) algorithm and compressed sampling
matching pursuit (CoSaMP) algorithm, the GSSK signals are
efficiently detected in the considered indoor GSSK-VLC system.
Furthermore, a more efficient detection algorithm combined with
OMP and maximum likelihood (ML) is also presented especially
for SSK scenario. Finally, the effectiveness of the proposed
sparsity aided detection algorithms in indoor GSSK-VLC system
are verified by computer simulations. The results show that the
proposed algorithms can achieve better bit error rate (BER) and
lower computation complexity than ML based detection method.
Specifically, a signal-to-noise ratio (SNR) gain as high as 12 dB is
observed in the SSK scenario and about 5 dB in case of a GSSK
scenario upon employing our proposed detection methods
A Compressed Sampling and Dictionary Learning Framework for WDM-Based Distributed Fiber Sensing
We propose a compressed sampling and dictionary learning framework for
fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is
generated from a model for the reflected sensor signal. Imperfect prior
knowledge is considered in terms of uncertain local and global parameters. To
estimate a sparse representation and the dictionary parameters, we present an
alternating minimization algorithm that is equipped with a pre-processing
routine to handle dictionary coherence. The support of the obtained sparse
signal indicates the reflection delays, which can be used to measure
impairments along the sensing fiber. The performance is evaluated by
simulations and experimental data for a fiber sensor system with common core
architecture.Comment: Accepted for publication in Journal of the Optical Society of America
A [ \copyright\ 2017 Optical Society of America.]. One print or electronic
copy may be made for personal use only. Systematic reproduction and
distribution, duplication of any material in this paper for a fee or for
commercial purposes, or modifications of the content of this paper are
prohibite
Spatial Coded Modulation
In this paper, we propose a spatial coded modulation (SCM) scheme, which
improves the accuracy of the active antenna detection by coding over the
transmit antennas. Specifically, the antenna activation pattern in the SCM
corresponds to a codeword in a properly designed codebook with a larger minimum
Hamming distance than its counterpart conventional spatial modulation. As the
minimum Hamming distance increases, the reliability of the active antenna
detection is directly enhanced, which in turn improves the demodulation of the
modulated symbols and yields a better system reliability. In addition to the
reliability, the proposed SCM scheme also achieves a higher capacity with the
identical antenna configuration compared to the conventional spatial modulation
technique. Moreover, the proposed SCM scheme strikes a balance between spectral
efficiency and reliability by trading off the minimum Hamming distance with the
number of available codewords. The optimal maximum likelihood detector is first
formulated. Then, a low-complexity suboptimal detector is proposed to reduce
the computational complexity, which has a two-step detection. Theoretical
derivations of the channel capacity and the bit error rate are presented in
various channel scenarios, i.e., Rayleigh, Rician, Nakagami-m, imperfect
channel state information, and spatial correlation. Further derivation on
performance bounding is also provided to reveal the insight of the benefit of
increasing the minimum Hamming distance. Numerical results validate the
analysis and demonstrate that the proposed SCM outperforms the conventional
spatial modulation techniques in both channel capacity and system reliability.Comment: 30 pages, 17 figure
Channel estimation techniques for filter bank multicarrier based transceivers for next generation of wireless networks
A dissertation submitted to Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the degree of Master of Science in Engineering (Electrical and Information Engineering), August 2017The fourth generation (4G) of wireless communication system is designed based on the principles of cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) where the cyclic prefix (CP) is used to combat inter-symbol interference (ISI) and inter-carrier interference (ICI) in order to achieve higher data rates in comparison to the previous generations of wireless networks. Various filter bank multicarrier systems have been considered as potential waveforms for the fast emerging next generation (xG) of wireless networks (especially the fifth generation (5G) networks). Some examples of the considered waveforms are orthogonal frequency division multiplexing with offset quadrature amplitude modulation based filter bank, universal filtered multicarrier (UFMC), bi-orthogonal frequency division multiplexing (BFDM) and generalized frequency division multiplexing (GFDM). In perfect reconstruction (PR) or near perfect reconstruction (NPR) filter bank designs, these aforementioned FBMC waveforms adopt the use of well-designed prototype filters (which are used for designing the synthesis and analysis filter banks) so as to either replace or minimize the CP usage of the 4G networks in order to provide higher spectral efficiencies for the overall increment in data rates. The accurate designing of the FIR low-pass prototype filter in NPR filter banks results in minimal signal distortions thus, making the analysis filter bank a time-reversed version of the corresponding synthesis filter bank. However, in non-perfect reconstruction (Non-PR) the analysis filter bank is not directly a time-reversed version of the corresponding synthesis filter bank as the prototype filter impulse response for this system is formulated (in this dissertation) by the introduction of randomly generated errors. Hence, aliasing and amplitude distortions are more prominent for Non-PR.
Channel estimation (CE) is used to predict the behaviour of the frequency selective channel and is usually adopted to ensure excellent reconstruction of the transmitted symbols. These techniques can be broadly classified as pilot based, semi-blind and blind channel estimation schemes. In this dissertation, two linear pilot based CE techniques namely the least square (LS) and linear minimum mean square error (LMMSE), and three adaptive channel estimation schemes namely least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS) are presented, analyzed and documented. These are implemented while exploiting the near orthogonality properties of offset quadrature amplitude modulation (OQAM) to mitigate the effects of interference for two filter bank waveforms (i.e. OFDM/OQAM and GFDM/OQAM) for the next generation of wireless networks assuming conditions of both NPR and Non-PR in slow and fast frequency selective Rayleigh fading channel. Results obtained from the computer simulations carried out showed that the channel estimation schemes performed better in an NPR filter bank system as compared with Non-PR filter banks. The low performance of Non-PR system is due to the amplitude distortion and aliasing introduced from the random errors generated in the system that is used to design its prototype filters. It can be concluded that RLS, NLMS, LMS, LMMSE and LS channel estimation schemes offered the best normalized mean square error (NMSE) and bit error rate (BER) performances (in decreasing order) for both waveforms assuming both NPR and Non-PR filter banks.
Keywords: Channel estimation, Filter bank, OFDM/OQAM, GFDM/OQAM, NPR, Non-PR, 5G, Frequency selective channel.CK201
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