2,263 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
Full-Duplex Non-Orthogonal Multiple Access for Modern Wireless Networks
Non-orthogonal multiple access (NOMA) is an interesting concept to provide
higher capacity for future wireless communications. In this article, we
consider the feasibility and benefits of combining full-duplex operation with
NOMA for modern communication systems. Specifically, we provide a comprehensive
overview on application of full-duplex NOMA in cellular networks, cooperative
and cognitive radio networks, and characterize gains possible due to
full-duplex operation. Accordingly, we discuss challenges, particularly the
self-interference and inter-user interference and provide potential solutions
to interference mitigation and quality-of-service provision based on
beamforming, power control, and link scheduling. We further discuss future
research challenges and interesting directions to pursue to bring full-duplex
NOMA into maturity and use in practice.Comment: Revised, IEEE Wireless Communication Magazin
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
As a key technique for enabling artificial intelligence, machine learning
(ML) is capable of solving complex problems without explicit programming.
Motivated by its successful applications to many practical tasks like image
recognition, both industry and the research community have advocated the
applications of ML in wireless communication. This paper comprehensively
surveys the recent advances of the applications of ML in wireless
communication, which are classified as: resource management in the MAC layer,
networking and mobility management in the network layer, and localization in
the application layer. The applications in resource management further include
power control, spectrum management, backhaul management, cache management,
beamformer design and computation resource management, while ML based
networking focuses on the applications in clustering, base station switching
control, user association and routing. Moreover, literatures in each aspect is
organized according to the adopted ML techniques. In addition, several
conditions for applying ML to wireless communication are identified to help
readers decide whether to use ML and which kind of ML techniques to use, and
traditional approaches are also summarized together with their performance
comparison with ML based approaches, based on which the motivations of surveyed
literatures to adopt ML are clarified. Given the extensiveness of the research
area, challenges and unresolved issues are presented to facilitate future
studies, where ML based network slicing, infrastructure update to support ML
based paradigms, open data sets and platforms for researchers, theoretical
guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure
Signal Processing and Optimal Resource Allocation for the Interference Channel
In this article, we examine several design and complexity aspects of the
optimal physical layer resource allocation problem for a generic interference
channel (IC). The latter is a natural model for multi-user communication
networks. In particular, we characterize the computational complexity, the
convexity as well as the duality of the optimal resource allocation problem.
Moreover, we summarize various existing algorithms for resource allocation and
discuss their complexity and performance tradeoff. We also mention various open
research problems throughout the article.Comment: To appear in E-Reference Signal Processing, R. Chellapa and S.
Theodoridis, Eds., Elsevier, 201
Interference Alignment in Multi-Input Multi-Output Cognitive Radio-Based Network
This study investigates the interference alignment techniques for cognitive radio networks toward 5G to meet the demand and challenges for future wireless communications requirements. In this context, we examine the performance of the interference alignment in two parts. In the first part of this chapter, a multi-input multi-output (MIMO) cognitive radio network in the presence of multiple secondary users (SUs) is investigated. The proposed model assumes that linear interference alignment is used at the primary system to lessen the interference between primary and secondary networks. Herein, we derive the closed-form mathematical equations for the outage probability considering the interference leakage occurred in the primary system. The second part of this study analyzes the performance of interference alignment for underlay cognitive two-way relay networks with channel state information (CSI) quantization error. Here, a two-way amplify-and-forward relaying scheme is considered for independent and identically distributed Rayleigh fading channel. The closed-form average pairwise error probability expressions are derived, and the effect of CSI quantization error is analyzed based on the bit error rate performance. Finally, we evaluate the instantaneous capacity for both primary and secondary networks*
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
Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer
Radio frequency (RF) energy harvesting and transfer techniques have recently
become alternative methods to power the next generation of wireless networks.
As this emerging technology enables proactive replenishment of wireless
devices, it is advantageous in supporting applications with quality-of-service
(QoS) requirement. This article focuses on the resource allocation issues in
wireless networks with RF energy harvesting capability, referred to as RF
energy harvesting networks (RF-EHNs). First, we present an overview of the
RF-EHNs, followed by a review of a variety of issues regarding resource
allocation. Then, we present a case study of designing in the receiver
operation policy, which is of paramount importance in the RF-EHNs. We focus on
QoS support and service differentiation, which have not been addressed by
previous literatures. Furthermore, we outline some open research directions.Comment: To appear in IEEE Networ
Cache-enabled Wireless Networks with Opportunistic Interference Alignment
Both caching and interference alignment (IA) are promising techniques for
future wireless networks. Nevertheless, most of existing works on cache-enabled
IA wireless networks assume that the channel is invariant, which is unrealistic
considering the time-varying nature of practical wireless environments. In this
paper, we consider realistic time-varying channels. Specifically, the channel
is formulated as a finite-state Markov channel (FSMC). The complexity of the
system is very high when we consider realistic FSMC models. Therefore, we
propose a novel big data reinforcement learning approach in this paper. Deep
reinforcement learning is an advanced reinforcement learning algorithm that
uses deep network to approximate the value-action function. Deep
reinforcement learning is used in this paper to obtain the optimal IA user
selection policy in cache-enabled opportunistic IA wireless networks.
Simulation results are presented to show the effectiveness of the proposed
scheme
Random Aerial Beamforming for Underlay Cognitive Radio with Exposed Secondary Users
In this paper, we introduce the exposed secondary users problem in underlay
cognitive radio systems, where both the secondary-to-primary and
primary-to-secondary channels have a Line-of-Sight (LoS) component. Based on a
Rician model for the LoS channels, we show, analytically and numerically, that
LoS interference hinders the achievable secondary user capacity when
interference constraints are imposed at the primary user receiver. This is
caused by the poor dynamic range of the interference channels fluctuations when
a dominant LoS component exists. In order to improve the capacity of such
system, we propose the usage of an Electronically Steerable Parasitic Array
Radiator (ESPAR) antennas at the secondary terminals. An ESPAR antenna involves
a single RF chain and has a reconfigurable radiation pattern that is controlled
by assigning arbitrary weights to M orthonormal basis radiation patterns via
altering a set of reactive loads. By viewing the orthonormal patterns as
multiple virtual dumb antennas, we randomly vary their weights over time
creating artificial channel fluctuations that can perfectly eliminate the
undesired impact of LoS interference. This scheme is termed as Random Aerial
Beamforming (RAB), and is well suited for compact and low cost mobile terminals
as it uses a single RF chain. Moreover, we investigate the exposed secondary
users problem in a multiuser setting, showing that LoS interference hinders
multiuser interference diversity and affects the growth rate of the SU capacity
as a function of the number of users. Using RAB, we show that LoS interference
can actually be exploited to improve multiuser diversity via opportunistic
nulling
Dealing with Limited Backhaul Capacity in Millimeter Wave Systems: A Deep Reinforcement Learning Approach
Millimeter Wave (MmWave) communication is one of the key technology of the
fifth generation (5G) wireless systems to achieve the expected 1000x data rate.
With large bandwidth at mmWave band, the link capacity between users and base
stations (BS) can be much higher compared to sub-6GHz wireless systems.
Meanwhile, due to the high cost of infrastructure upgrade, it would be
difficult for operators to drastically enhance the capacity of backhaul links
between mmWave BSs and the core network. As a result, the data rate provided by
backhaul may not be sufficient to support all mmWave links, the backhaul
connection becomes the new bottleneck that limits the system performance. On
the other hand, as mmWave channels are subject to random blockage, the data
rates of mmWave users significantly vary over time. With limited backhaul
capacity and highly dynamic data rates of users, how to allocate backhaul
resource to each user remains a challenge for mmWave systems. In this article,
we present a deep reinforcement learning (DRL) approach to address this
challenge. By learning the blockage pattern, the system dynamics can be
captured and predicted, resulting in efficient utilization of backhaul
resource. We begin with a discussion on DRL and its application in wireless
systems. We then investigate the problem backhaul resource allocation and
present the DRL based solution. Finally, we discuss open problems for future
research and conclude this article.Comment: Appear to IEEE Communications Magazine. Version with math contents
and equation
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