45 research outputs found
A Survey of Physical Layer Security Techniques for 5G Wireless Networks and Challenges Ahead
Physical layer security which safeguards data confidentiality based on the
information-theoretic approaches has received significant research interest
recently. The key idea behind physical layer security is to utilize the
intrinsic randomness of the transmission channel to guarantee the security in
physical layer. The evolution towards 5G wireless communications poses new
challenges for physical layer security research. This paper provides a latest
survey of the physical layer security research on various promising 5G
technologies, including physical layer security coding, massive multiple-input
multiple-output, millimeter wave communications, heterogeneous networks,
non-orthogonal multiple access, full duplex technology, etc. Technical
challenges which remain unresolved at the time of writing are summarized and
the future trends of physical layer security in 5G and beyond are discussed.Comment: To appear in IEEE Journal on Selected Areas in Communication
Communication Over MIMO Broadcast Channels Using Lattice-Basis Reduction
A simple scheme for communication over MIMO broadcast channels is introduced
which adopts the lattice reduction technique to improve the naive channel
inversion method. Lattice basis reduction helps us to reduce the average
transmitted energy by modifying the region which includes the constellation
points. Simulation results show that the proposed scheme performs well, and as
compared to the more complex methods (such as the perturbation method) has a
negligible loss. Moreover, the proposed method is extended to the case of
different rates for different users. The asymptotic behavior of the symbol
error rate of the proposed method and the perturbation technique, and also the
outage probability for the case of fixed-rate users is analyzed. It is shown
that the proposed method, based on LLL lattice reduction, achieves the optimum
asymptotic slope of symbol-error-rate (called the precoding diversity). Also,
the outage probability for the case of fixed sum-rate is analyzed.Comment: Submitted to IEEE Trans. on Info. Theory (Jan. 15, 2006), Revised
(Jun. 12, 2007
Joint Waveform and Passive Beamformer Design in Multi-IRS-Aided Radar
Intelligent reflecting surface (IRS) technology has recently attracted a
significant interest in non-light-of-sight radar remote sensing. Prior works
have largely focused on designing single IRS beamformers for this problem. For
the first time in the literature, this paper considers multi-IRS-aided
multiple-input multiple-output (MIMO) radar and jointly designs the transmit
unimodular waveforms and optimal IRS beamformers. To this end, we derive the
Cramer-Rao lower bound (CRLB) of target direction-of-arrival (DoA) as a
performance metric. Unimodular transmit sequences are the preferred waveforms
from a hardware perspective. We show that, through suitable transformations,
the joint design problem can be reformulated as two unimodular quadratic
programs (UQP). To deal with the NP-hard nature of both UQPs, we propose
unimodular waveform and beamforming design for multi-IRS radar (UBeR) algorithm
that takes advantage of the low-cost power method-like iterations. Numerical
experiments illustrate that the MIMO waveforms and phase shifts obtained from
our UBeR algorithm are effective in improving the CRLB of DoA estimation
A survey on hybrid beamforming techniques in 5G : architecture and system model perspectives
The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multiple-output (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energy-efficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers' structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks. We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain
Lattice reduction and list based low complexity MIMO detection and its applications.
Multiple input multiple output (MIMO) is an important technique of improving the spectral efficiency in wireless communications. In MIMO systems, it is usually required to jointly detect signals at the receiver. While the maximum likelihood (ML) MIMO detection provides an optimal performance with full receive diversity, its complexity grows exponentially with the number of transmit antennas. Thus, lattice reduction (LR) and list based detectors are developed to reduce the complexity. In this thesis, we first apply the partial maximum a posteriori probability (PMAP) principle to the list-based method for MIMO detection. It shows that the PMAP-based list detection outperforms the conventional list detection with a reasonably low complexity. To further improve the performance for slow fading MIMO channels, we develop the column reordering criteria (CRC) for the LR-based list detection. It shows that with our proposed CRC, the LR,-based list detection can provide a near ML performance with a sufficiently low complexity. Then, we develop a complexity efficient pre-voting cancellation based detection with pre-voting vector selection criteria for underdetermined MIMO systems and show that this scheme can exploit a near ML performance with full receive diversity. An extension of MIMO systems is multiuser MIMO systems, where the user selection becomes an effective way to increase diversity (multiuser diversity). If multiple users are selected to access the channel at a time, the selection problem becomes a combinatorial problem, where an exhaustive search may leads to highly computational complexity. Therefore, we propose a low complexity greedy user selection scheme with an iterative LR updating algorithm when a LR-based MIMO detector is used. It shows that the proposed selection scheme can provide a comparable performance to the combinatorial ones with much lower complexity
Polynomial matrix decomposition techniques for frequency selective MIMO channels
For a narrowband, instantaneous mixing multi-input, multi-output (MIMO) communications system,
the channel is represented as a scalar matrix. In this scenario, singular value decomposition (SVD)
provides a number of independent spatial subchannels which can be used to enhance data rates or to increase diversity. Alternatively, a QR decomposition can be used to reduce the MIMO channel equalization problem to a set of single channel equalization problems.
In the case of a frequency selective MIMO system, the multipath channel is represented as a polynomial matrix. Thus conventional matrix decomposition techniques can no longer be applied. The traditional solution to this broadband problem is to reduce it to narrowband form by using a discrete Fourier transform (DFT) to split the broadband channel into N narrow uniformly spaced frequency bands and applying scalar decomposition techniques within each band. This describes an orthogonal frequency division multiplexing (OFDM) based system.
However, a novel algorithm has been developed for calculating the eigenvalue decomposition of a
para-Hermitian polynomial matrix, known as the sequential best rotation (SBR2) algorithm. SBR2
and its QR based derivatives allow a true polynomial singular value and QR decomposition to be
formulated. The application of these algorithms within frequency selective MIMO systems results in
a fundamentally new approach to exploiting spatial diversity.
Polynomial matrix decomposition and OFDM based solutions are compared for a wide variety of
broadband MIMO communication systems. SVD is used to create a robust, high gain communications
channel for ultra low
signal-to-noise ratio (SNR) environments. Due to the frequency selective nature
of the channels produced by polynomial matrix decomposition, additional processing is required at the receiver resulting in two distinct equalization techniques based around turbo and Viterbi equalization. The proposed approach is found to provide identical performance to that of an existing OFDM scheme while supporting a wider range of access schemes. This work is then extended to QR decomposition
based communications systems, where the proposed polynomial approach is found to not only provide superior bit-error-rate (BER) performance but significantly reduce the complexity of transmitter
design. Finally both techniques are combined to create a nulti-user MIMO system that provides superior BER performance over an OFDM based scheme. Throughout the work the robustness of the proposed scheme to channel state information (CSI) error is considered, resulting in a rigorous
demonstration of the capabilities of the polynomial approach
Applications of Lattices over Wireless Channels
In wireless networks, reliable communication is a challenging issue due to many attenuation factors such as receiver noise, channel fading, interference and asynchronous delays. Lattice coding and decoding provide efficient solutions to many problems in wireless communications and multiuser information theory. The capability in achieving the fundamental limits, together with simple and efficient transmitter and receiver structures, make the lattice strategy a promising approach. This work deals with problems of lattice detection over fading channels and time asynchronism over the lattice-based compute-and-forward protocol.
In multiple-input multiple-output (MIMO) systems, the use of lattice reduction significantly improves the performance of approximate detection techniques. In the first part of this thesis, by taking advantage of the temporal correlation of a Rayleigh fading channel, low complexity lattice reduction methods are investigated. We show that updating the reduced lattice basis adaptively with a careful use of previous channel realizations yields a significant saving in complexity with a minimal degradation in performance. Considering high data rate MIMO systems, we then investigate soft-output detection methods. Using the list sphere decoder (LSD) algorithm, an adaptive method is proposed to reduce the complexity of generating the list for evaluating the log-likelihood ratio (LLR) values.
In the second part, by applying the lattice coding and decoding schemes over asynchronous networks, we study the impact of asynchronism on the compute-and-forward strategy. While the key idea in compute-and-forward is to decode a linear synchronous combination of transmitted codewords, the distributed relays receive random asynchronous versions of the combinations. Assuming different asynchronous models, we design the receiver structure prior to the decoder of compute-and-forward so that the achievable rates are maximized at any signal-to-noise-ratio (SNR). Finally, we consider symbol-asynchronous X networks with single antenna nodes over time-invariant channels. We exploit the asynchronism among the received signals in order to design the interference alignment scheme. It is shown that the asynchronism provides correlated channel variations which are proved to be sufficient to implement the vector interference alignment over the constant X network