330 research outputs found
Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks
With years of tremendous traffic and energy consumption growth, green radio
has been valued not only for theoretical research interests but also for the
operational expenditure reduction and the sustainable development of wireless
communications. Fundamental green tradeoffs, served as an important framework
for analysis, include four basic relationships: spectrum efficiency (SE) versus
energy efficiency (EE), deployment efficiency (DE) versus energy efficiency
(EE), delay (DL) versus power (PW), and bandwidth (BW) versus power (PW). In
this paper, we first provide a comprehensive overview on the extensive on-going
research efforts and categorize them based on the fundamental green tradeoffs.
We will then focus on research progresses of 4G and 5G communications, such as
orthogonal frequency division multiplexing (OFDM) and non-orthogonal
aggregation (NOA), multiple input multiple output (MIMO), and heterogeneous
networks (HetNets). We will also discuss potential challenges and impacts of
fundamental green tradeoffs, to shed some light on the energy efficient
research and design for future wireless networks.Comment: revised from IEEE Communications Surveys & Tutorial
A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends
Non-orthogonal multiple access (NOMA) is an essential enabling technology for
the fifth generation (5G) wireless networks to meet the heterogeneous demands
on low latency, high reliability, massive connectivity, improved fairness, and
high throughput. The key idea behind NOMA is to serve multiple users in the
same resource block, such as a time slot, subcarrier, or spreading code. The
NOMA principle is a general framework, and several recently proposed 5G
multiple access schemes can be viewed as special cases. This survey provides an
overview of the latest NOMA research and innovations as well as their
applications. Thereby, the papers published in this special issue are put into
the content of the existing literature. Future research challenges regarding
NOMA in 5G and beyond are also discussed.Comment: to appear in IEEE JSAC, 201
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues
As a promising paradigm to reduce both capital and operating expenditures,
the cloud radio access network (C-RAN) has been shown to provide high spectral
efficiency and energy efficiency. Motivated by its significant theoretical
performance gains and potential advantages, C-RANs have been advocated by both
the industry and research community. This paper comprehensively surveys the
recent advances of C-RANs, including system architectures, key techniques, and
open issues. The system architectures with different functional splits and the
corresponding characteristics are comprehensively summarized and discussed. The
state-of-the-art key techniques in C-RANs are classified as: the fronthaul
compression, large-scale collaborative processing, and channel estimation in
the physical layer; and the radio resource allocation and optimization in the
upper layer. Additionally, given the extensiveness of the research area, open
issues and challenges are presented to spur future investigations, in which the
involvement of edge cache, big data mining, social-aware device-to-device,
cognitive radio, software defined network, and physical layer security for
C-RANs are discussed, and the progress of testbed development and trial test
are introduced as well.Comment: 27 pages, 11 figure
Optimizing IoT Energy Efficiency on Edge (EEE): a Cross-layer Design in a Cognitive Mesh Network
Battery-powered wireless IoT devices are now widely seen in many critical
applications. Given the limited battery capacity and inaccessibility to
external power recharge, optimizing energy efficiency (EE) plays a vital role
in prolonging the lifetime of these IoT devices. However, a sheer amount of
existing works only focus on the EE design at the infrastructure level such as
base stations (BSs) but with little attention to the EE design at the device
level. In this paper, we propose a novel idea that aims to shift energy
consumption to a grid-powered cognitive radio mesh network thus preserving
energy of battery-powered devices. Under this line of thinking, we cast the
design into a cross-layer optimization problem with an objective to maximize
devices' energy efficiency. To solve this problem, we propose a parametric
transformation technique to convert the original problem into a more tractable
one. A baseline scheme is used to demonstrate the advantage of our design. We
also carry out extensive simulations to exhibit the optimality of our proposed
algorithms and the network performance under various settings
Cognitive Wireless Powered Network: Spectrum Sharing Models and Throughput Maximization
The recent advance in radio-frequency (RF) wireless energy transfer (WET) has
motivated the study of wireless powered communication network (WPCN), in which
distributed wireless devices are powered via dedicated WET by the hybrid
access-point (H-AP) in the downlink (DL) for uplink (UL) wireless information
transmission (WIT). In this paper, by exploiting the cognitive radio (CR)
technique, we study a new type of CR enabled secondary WPCN, called cognitive
WPCN, under spectrum sharing with the primary wireless communication system. In
particular, we consider a cognitive WPCN, consisting of one single H-AP with
constant power supply and distributed users, shares the same spectrum for its
DL WET and UL WIT with an existing primary communication link, where the WPCN's
WET/WIT and the primary link's WIT may interfere with each other. Under this
new setup, we propose two coexisting models for spectrum sharing of the two
systems, namely underlay and overlay based cognitive WPCNs, depending on
different types of knowledge on the primary user transmission available at the
cognitive WPCN. For each model, we maximize the sum-throughput of the cognitive
WPCN by optimizing its transmission under different constraints applied to
protect the primary user transmission. Analysis and simulation results are
provided to compare the sum-throughput of the cognitive WPCN versus the
achievable rate of the primary user in two coexisting models. It is shown that
the overlay based cognitive WPCN outperforms the underlay based counterpart,
thanks to its fully cooperative WET/WIT design with the primary WIT, while it
also requires higher complexity for implementation.Comment: This is the longer version of a paper to appear in IEEE Transactions
on Cognitive Communications and Networkin
Throughput Enhancement of Multicarrier Cognitive M2M Networks: Universal-Filtered OFDM Systems
We consider a cognitive radio network consisting of a primary cellular system
and a secondary cognitive machine-to-machine (M2M) system, and study the
throughput enhancement problem of the latter system employing
universal-filtered orthogonal frequency division multiplexing (UF-OFDM)
modulation. The downlink transmission capacity of the cognitive M2M system is
thereby maximized, while keeping the interference introduced to the primary
users (PUs) below the pre-specified threshold, under total transmit power
budget of the secondary base station (SBS). The performance of UF-OFDM based CR
system is compared to the performances of OFDM-based and filter bank
multicarrier (FBMC)-based CR systems. We also propose a near-optimal resource
allocation method separating the subband and power allocation. The solution is
less complex compared to optimization of the original combinatorial problem. We
present numerical results that show that for given interference thresholds of
the PUs and maximum transmit power limit of the SBS, the UF-OFDM based CR
system exhibits intermediary performance in terms of achievable capacity
compared to OFDM and FBMC-based CR systems. Interestingly, for a certain degree
of robustness of the PUs, the UF-OFDM performs equally well as FBMC.
Furthermore, the percentage rate-gain of UF-OFDM based CR system increases by a
large amount when UF-OFDM modulation with lower sidelobes ripple is employed.
Numerical results also show that the proposed throughput enhancing method
despite having lower computational complexity compared to the optimal solution
achieves near-optimal performance
Capacity and performance analysis for multi-user system under distributed opportunistic scheduling in a time dependent channel
Consider the problem of a multi-user multiple access channel. While several
multi-user coding techniques exist, in practical scenarios, not all users can
be scheduled simultaneously. Thus, a key problem is which users to schedule in
a given time slot. Under realistic approach for time dependency of the channel,
we adopt a distributed scheduling algorithm in which each user, in the
beginning of each slot, estimates his channel gain and compares it to a
threshold, and if exceeding it the user can transmit. In this work we are
interested in the expected capacity of the system and the delay and quality of
service of the data accumulated at the users under this scheduling scheme.
First we derive the expected capacity under scheduling (distributed and
centralized) for this time dependent environment and show that its scaling law
is , were are the good
channel parameters (assuming Gaussian capacity approximation, e.g., under MIMO)
and is the number of users. Then we turn to the performance analysis of
such system while assuming the users are not necessarily fully backlogged, and
focus specifically on the queueing problem and the strong dependence between
the queues which leave no alternative but to turn to approximate models for
this system. We adopt the celebrated model of Ephremides and Zhu to give new
results on the convergence of the probability of collision to its average value
(as the number of users grows), and hence for the ensuing system performance
metrics, such as throughput and delay. We further utilize this finding to
suggest a much simpler approximate model, which accurately describes the system
behavior when the number of queues is large. The system performance as
predicted by the approximate models shows excellent agreement with simulation
results
Joint Spatial Division and Multiplexing
We propose Joint Spatial Division and Multiplexing (JSDM), an approach to
multiuser MIMO downlink that exploits the structure of the correlation of the
channel vectors in order to allow for a large number of antennas at the base
station while requiring reduced-dimensional Channel State Information at the
Transmitter (CSIT). This allows for significant savings both in the downlink
training and in the CSIT feedback from the user terminals to the base station,
thus making the use of a large number of base station antennas potentially
suitable also for Frequency Division Duplexing (FDD) systems, for which
uplink/downlink channel reciprocity cannot be exploited. JSDM forms the
multiuser MIMO downlink precoder by concatenating a pre-beamforming matrix,
which depends only on the channel second-order statistics, with a classical
multiuser precoder, based on the instantaneous knowledge of the resulting
reduced dimensional effective channels. We prove a simple condition under which
JSDM incurs no loss of optimality with respect to the full CSIT case. For
linear uniformly spaced arrays, we show that such condition is closely
approached when the number of antennas is large. For this case, we use Szego
asymptotic theory of large Toeplitz matrices to design a DFT-based
pre-beamforming scheme requiring only coarse information about the users angles
of arrival and angular spread. Finally, we extend these ideas to the case of a
two-dimensional base station antenna array, with 3-dimensional beamforming,
including multiple beams in the elevation angle direction. We provide
guidelines for the pre-beamforming optimization and calculate the system
spectral efficiency under proportional fairness and maxmin fairness criteria,
showing extremely attractive performance. Our numerical results are obtained
via an asymptotic random matrix theory tool known as deterministic equivalent
approximation.Comment: 10 figure
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
Delay-Constrained Video Transmission: Quality-driven Resource Allocation and Scheduling
Real-time video demands quality-of-service (QoS) guarantees such as delay
bounds for end-user satisfaction. Furthermore, the tolerable delay varies
depending on the use case such as live streaming or two-way video conferencing.
Due to the inherently stochastic nature of wireless fading channels,
deterministic delay bounds are difficult to guarantee. Instead, we propose
providing statistical delay guarantees using the concept of effective capacity.
We consider a multiuser setup whereby different users have (possibly different)
delay QoS constraints. We derive the resource allocation policy that maximizes
the sum video quality and applies to any quality metric with concave
rate-quality mapping. We show that the optimal operating point per user is such
that the rate-distortion slope is the inverse of the supported video source
rate per unit bandwidth, a key metric we refer to as the source spectral
efficiency. We also solve the alternative problem of fairness-based resource
allocation whereby the objective is to maximize the minimum video quality
across users. Finally, we derive user admission and scheduling policies that
enable selecting a maximal user subset such that all selected users can meet
their statistical delay requirement. Results show that video users with
differentiated QoS requirements can achieve similar video quality with vastly
different resource requirements. Thus, QoS-aware scheduling and resource
allocation enable supporting significantly more users under the same resource
constraints.Comment: Submitted to IEEE Journal of Selected Topics in Signal Processin
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