1,022 research outputs found
A Survey on MIMO Transmission with Discrete Input Signals: Technical Challenges, Advances, and Future Trends
Multiple antennas have been exploited for spatial multiplexing and diversity
transmission in a wide range of communication applications. However, most of
the advances in the design of high speed wireless multiple-input multiple
output (MIMO) systems are based on information-theoretic principles that
demonstrate how to efficiently transmit signals conforming to Gaussian
distribution. Although the Gaussian signal is capacity-achieving, signals
conforming to discrete constellations are transmitted in practical
communication systems. As a result, this paper is motivated to provide a
comprehensive overview on MIMO transmission design with discrete input signals.
We first summarize the existing fundamental results for MIMO systems with
discrete input signals. Then, focusing on the basic point-to-point MIMO
systems, we examine transmission schemes based on three most important criteria
for communication systems: the mutual information driven designs, the mean
square error driven designs, and the diversity driven designs. Particularly, a
unified framework which designs low complexity transmission schemes applicable
to massive MIMO systems in upcoming 5G wireless networks is provided in the
first time. Moreover, adaptive transmission designs which switch among these
criteria based on the channel conditions to formulate the best transmission
strategy are discussed. Then, we provide a survey of the transmission designs
with discrete input signals for multiuser MIMO scenarios, including MIMO uplink
transmission, MIMO downlink transmission, MIMO interference channel, and MIMO
wiretap channel. Additionally, we discuss the transmission designs with
discrete input signals for other systems using MIMO technology. Finally,
technical challenges which remain unresolved at the time of writing are
summarized and the future trends of transmission designs with discrete input
signals are addressed.Comment: 110 pages, 512 references, submit to Proceedings of the IEE
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
Low-Complexity Channel Estimation with Set-Membership Algorithms for Cooperative Wireless Sensor Networks
In this paper, we consider a general cooperative wireless sensor network
(WSN) with multiple hops and the problem of channel estimation. Two
matrix-based set-membership algorithms are developed for the estimation of the
complex matrix channel parameters. The main goal is to reduce the computational
complexity significantly as compared with existing channel estimators and
extend the lifetime of the WSN by reducing its power consumption. The first
proposed algorithm is the set-membership normalized least mean squares
(SM-NLMS) algorithm. The second is the set-membership recursive least squares
(RLS) algorithm called BEACON. Then, we present and incorporate an error bound
function into the two channel estimation methods which can adjust the error
bound automatically with the update of the channel estimates. Steady-state
analysis in the output mean-squared error (MSE) are presented and closed-form
formulae for the excess MSE and the probability of update in each recursion are
provided. Computer simulations show good performance of our proposed algorithms
in terms of convergence speed, steady state mean square error and bit error
rate (BER) and demonstrate reduced complexity and robustness against the
time-varying environments and different signal-to-noise ratio (SNR) values.Comment: 15 Figure
Deep Learning for Wireless Communications
Existing communication systems exhibit inherent limitations in translating
theory to practice when handling the complexity of optimization for emerging
wireless applications with high degrees of freedom. Deep learning has a strong
potential to overcome this challenge via data-driven solutions and improve the
performance of wireless systems in utilizing limited spectrum resources. In
this chapter, we first describe how deep learning is used to design an
end-to-end communication system using autoencoders. This flexible design
effectively captures channel impairments and optimizes transmitter and receiver
operations jointly in single-antenna, multiple-antenna, and multiuser
communications. Next, we present the benefits of deep learning in spectrum
situation awareness ranging from channel modeling and estimation to signal
detection and classification tasks. Deep learning improves the performance when
the model-based methods fail. Finally, we discuss how deep learning applies to
wireless communication security. In this context, adversarial machine learning
provides novel means to launch and defend against wireless attacks. These
applications demonstrate the power of deep learning in providing novel means to
design, optimize, adapt, and secure wireless communications
Enhanced Multi-Parameter Cognitive Architecture for Future Wireless Communications
The very original concept of cognitive radio (CR) raised by Mitola targets at
all the environment parameters, including those in physical layer, MAC layer,
application layer as well as the information extracted from reasoning. Hence
the first CR is also referred to as "full cognitive radio". However, due to its
difficult implementation, FCC and Simon Haykin separately proposed a much more
simplified definition, in which CR mainly detects one single parameter, i.e.,
spectrum occupancy, and is also called as "spectrum sensing cognitive radio".
With the rapid development of wireless communication, the infrastructure of a
wireless system becomes much more complicated while the functionality at every
node is desired to be as intelligent as possible, say the self-organized
capability in the approaching 5G cellular networks. It is then interesting to
re-look into Mitola's definition and think whether one could, besides obtaining
the "on/off" status of the licensed user only, achieve more parameters in a
cognitive way. In this article, we propose a new cognitive architecture
targeting at multiple parameters in future cellular networks, which is a one
step further towards the "full cognition" compared to the most existing CR
research. The new architecture is elaborated in detailed stages, and three
representative examples are provided based on the recent research progress to
illustrate the feasibility as well as the validity of the proposed
architecture.Comment: 15 pages, 6 figures, IEEE Communications Magazin
Channel Estimation Error, Oscillator Stability And Wireless Power Transfer In Wireless Communication With Distributed Reception Networks
This dissertation considers three related problems in distributed transmission and reception networks. Generally speaking, these types of networks have a transmit cluster with one or more transmit nodes and a receive cluster with one or more receive nodes. Nodes within a given cluster can communicate with each other using a wired or wireless local area network (LAN/WLAN). The overarching goal in this setting is typically to increase the efficiency of communication between the transmit and receive clusters through techniques such as distributed transmit beamforming, distributed reception, or other distributed versions of multi-input multi-output (MIMO) communication. More recently, the problem of wireless power transfer has also been considered in this setting.
The first problem considered by this dissertation relates to distributed reception in a setting with a single transmit node and multiple receive nodes. Since exchanging lightly quantized versions of in-phase and quadrature samples results in high throughput requirements on the receive LAN/WLAN, previous work has considered an approach where nodes exchange hard decisions, along with channel magnitudes, to facilitate combining similar to an ideal receive beamformer. It has been shown that this approach leads to a small loss in SNR performance, with large reductions in required LAN/WLAN throughput. A shortcoming of this work, however, is that all of the prior work has assumed that each receive node has a perfect estimation of its channel to the transmitter.
To address this shortcoming, the first part of this dissertation investigates the effect of channel estimation error on the SNR performance of distributed reception. Analytical expressions for these effects are obtained for two different modulation schemes, M-PSK and M2-QAM. The analysis shows the somewhat surprising result that channel estimation error causes the same amount of performance degradation in ideal beamforming and pseudo-beamforming systems despite the fact that the channel estimation errors manifests themselves quite differently in both systems.
The second problem considered in this dissertation is related to oscillator stability and phase noise modeling. In distributed transmission systems with multiple transmitters in the transmit cluster, synchronization requirements are typically very strict, e.g., on the order of one picosecond, to maintain radio frequency phase alignment across transmitters. Therefore, being able to accurately model the behavior of the oscillators and their phase noise responses is of high importance. Previous approaches have typically relied on a two-state model, but this model is often not sufficiently rich to model low-cost oscillators. This dissertation develops a new three-state oscillator model and a method for estimating the parameters of this model from experimental data. Experimental results show that the proposed model provides up to 3 dB improvement in mean squared error (MSE) performance with respect to a two-state model.
The last part of this work is dedicated to the problem of wireless power transfer in a setting with multiple nodes in the transmit cluster and multiple nodes in the receive cluster. The problem is to align the phases of the transmitters to achieve a certain power distribution across the nodes in the receive cluster. To find optimum transmit phases, we consider a iterative approach, similar to the prior work on one-bit feedback for distributed beamforming, in which each receive node sends a one-bit feedback to the transmit cluster indicating if the received power in that time slot for that node is increased. The transmitters then update their phases based on the feedback. What makes this problem particularly interesting is that, unlike the prior work on one-bit feedback for distributed beamforming, this is a multi-objective optimization problem where not every receive node can receive maximum power from the transmit array. Three different phase update decision rules, each based on the one-bit feedback signals, are analyzed. The effect of array sparsity is also investigated in this setting
A Journey from Improper Gaussian Signaling to Asymmetric Signaling
The deviation of continuous and discrete complex random variables from the
traditional proper and symmetric assumption to a generalized improper and
asymmetric characterization (accounting correlation between a random entity and
its complex conjugate), respectively, introduces new design freedom and various
potential merits. As such, the theory of impropriety has vast applications in
medicine, geology, acoustics, optics, image and pattern recognition, computer
vision, and other numerous research fields with our main focus on the
communication systems. The journey begins from the design of improper Gaussian
signaling in the interference-limited communications and leads to a more
elaborate and practically feasible asymmetric discrete modulation design. Such
asymmetric shaping bridges the gap between theoretically and practically
achievable limits with sophisticated transceiver and detection schemes in both
coded/uncoded wireless/optical communication systems. Interestingly,
introducing asymmetry and adjusting the transmission parameters according to
some design criterion render optimal performance without affecting the
bandwidth or power requirements of the systems. This dual-flavored article
initially presents the tutorial base content covering the interplay of
reality/complexity, propriety/impropriety and circularity/noncircularity and
then surveys majority of the contributions in this enormous journey.Comment: IEEE COMST (Early Access
Position Information-based NOMA for Downlink and Uplink Transmission in Mobile Scenarios
In this paper, a non-orthogonal multiple access (NOMA) system with partial
channel state information (CSI) for downlink and uplink transmission in mobile
scenarios is considered, i.e., users are deployed randomly and will move
casually around the base station (BS). In this case, the channel gain of each
user varies over time, which has an influence on the performance of
conventional NOMA. An analytical framework is developed to evaluate the impact
of position estimation deviation in terms of decoding order error probability,
average sum rate and outage probability. Based on the framework, dynamic power
allocation (DPA) for downlink NOMA and dynamic power control (DPC) for uplink
NOMA are put forward to optimize the outage performance with user distance
information. It has been shown that the performance of NOMA relies on accurate
user position information. To this end, two algorithms based on position
filtering are proposed to improve the accuracy of user position. Monte Carlo
simulation is presented to demonstrate the improvement of spectrum efficiency
and outage performance. Simulation results verify the accuracy of the proposed
analytical framework.Comment: To appear in the IEEE Acces
The Convergence of Machine Learning and Communications
The areas of machine learning and communication technology are converging.
Today's communications systems generate a huge amount of traffic data, which
can help to significantly enhance the design and management of networks and
communication components when combined with advanced machine learning methods.
Furthermore, recently developed end-to-end training procedures offer new ways
to jointly optimize the components of a communication system. Also in many
emerging application fields of communication technology, e.g., smart cities or
internet of things, machine learning methods are of central importance. This
paper gives an overview over the use of machine learning in different areas of
communications and discusses two exemplar applications in wireless networking.
Furthermore, it identifies promising future research topics and discusses their
potential impact.Comment: 8 pages, 4 figure
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